A Review of Assessment Methods for Coastal Hydro-Environmental Processes: Research Trends and Challenges

Abstract

Coastal regions face escalating challenges, including climate change, rapid urbanisation, ocean pollution, habitat degradation, and nutrient enrichment, which threaten coastal ecosystem health, biodiversity, and human livelihoods. A comprehensive understanding of coastal hydro-environmental processes, encompassing hydrodynamics, sediment transport driven by waves and currents, and biogeochemical dynamics influencing water quality, is essential for sustainable coastal management. This study presents a global systematic review of assessment methods for these processes, focusing on field data collection, laboratory experiments, numerical modelling, and artificial intelligence techniques. A bibliometric analysis was conducted on 165 peer-reviewed articles from Scopus and Web of Science, adhering to PRISMA 2020 guidelines. The findings reveal a significant shift from conventional standalone methods to integrated approaches, with 31.5% of studies combining field data with numerical models and 20% incorporating AI with field data, emphasising the need for real-time data integration and interdisciplinary strategies to enhance model reliability. This study also introduces a novel process–method–time classification framework that functionally aligns various assessment methods with specific coastal processes. However, challenges such as limited long-term datasets, high computational costs, and data resolution constraints persist. By synthesising global research trends and methodological advancements, this study offers critical insights to support more resilient, adaptive, and data-driven coastal management strategies.

1. Introduction

Coastal regions encompass a wide range of environments, including beaches, estuaries, mangroves, lagoons, deltas, and coral reefs that support diverse ecosystems. They are critically significant for global biodiversity, climate regulation, and economic activities [1,2]. These areas support diverse habitats for both flora and fauna and play a vital role in protecting inland regions from coastal hazards, such as flooding caused by storm surges, wave overtopping, defence beaches, and tsunamis [3,4]. Additionally, coastal zones are densely populated regions with a substantial portion of the global population residing in these areas. These areas have a significant socio-economic impact, supporting agriculture, fishing, aquaculture, oil and gas exploitation, transportation, and recreation [5,6]. Coastal areas are characterised by complex hydro-environmental processes driven by dynamic interactions among physical coastal features, ecosystems, and natural and human-induced influences [7,8,9]. The critical components of the processes include hydrodynamic processes, sediment transport, and biogeochemical processes, which are interrelated. Coastal morphology can alter hydrodynamic patterns and ecological distributions [1], while natural forces and human activities continuously reshape the coastal landscape [10], highlighting a dynamic and mutually influential relationship. Hydrodynamic processes involve the movement of waves, tides, and currents, which drive sediment transport and nutrient distribution along coastal regions [11,12]. Sand, silt, and other particles moving through tidal forces and wave action may cause either accretion or erosion in coastal areas, leading to continuous reshaping of the coastal landscape [13]. The phenomenon is intense in coastal lagoons where wind- and tide-induced currents influence sediment deposition in tidal inlets, affecting the hydrodynamics of these areas [14]. Moreover, nutrients, pollutants, and organic matter, which are attached to the sediment, may disperse along coastal areas, influencing the health of coastal ecosystems [15,16]. Widespread coastal development and the growing impacts of climate change have contributed to coastal environment degradation, resulting in shoreline erosion and habitat degradation [17,18,19]. Therefore, a critical assessment of the processes is essential to mitigate these issues through effective coastal conservation and management.

The methods of assessing coastal hydro-environmental processes have evolved significantly, enabling more profound insights into the interactions between coastal waters and their surrounding environments [20]. Most of the studies have implemented combined assessment techniques, including field data observation and collection, physical model experiment tests, numerical models, and artificial intelligence tools to evaluate the parameters of the processes [21,22]. Field instruments such as Acoustic Doppler Current Profilers (ADCPs), wave buoys, and sensors are utilised to measure wave heights, tidal flows, and water quality parameters [23,24]. Remote sensing techniques, including satellite imagery, video images, and Real-Time Kinematic Global Positioning System (RTK GPS), are recommended to predict coastal morphology accurately [25,26,27]. On the other hand, researchers utilised physical models, such as small-scale flumes and large wave basins, to simulate and analyse the hydro-environmental processes [28,29]. Numerical models are then developed to simulate various scenarios of hydrodynamics, sediment transport, and biogeochemical processes in coastal environments, aiding in environmental impact assessments and decision-making processes [21,30,31,32,33]. Models like DELFT3D, MIKE 21/3, and 3-D hydrodynamic Finite Volume Coastal Ocean Model (FVCOM) aid in assessing coastal hydrodynamics, coastal morphodynamics, and biogeochemical processes [24,34,35,36,37,38,39]. Moreover, to improve the prediction of coastal hydro-environmental processes and evaluate the long-term impacts on the coastal environments, artificial intelligence tools such as Gaussian Process Regression (GPR), Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs) are introduced [40,41,42,43,44,45]. Thus, a holistic approach to coastal hydro-environmental processes is essential for sustainable coastal management and preservation [38,46,47].

This study aims to review the assessment methods used for coastal hydro-environmental processes by identifying current research trends, existing gaps, and methodological challenges. A global systematic review and bibliometric analysis were conducted, focusing on four primary methodological fields of application: field survey techniques, experiment models, numerical simulations, and advanced artificial intelligence (AI)-based tools. This review also introduces a novel classification of assessment methods based on their application to specific key coastal processes such as hydrodynamic, sediment transport, and biogeochemical processes, providing guidance for future research and tool selection. Comprehensive method-specific bibliometric analyses are conducted to support the classification framework, revealing emerging trends across the assessment methods. This study contributes a dual perspective: a detailed evaluation of individual methods and a broader meta-perspective on emerging integration patterns that have not been comprehensively addressed in previous reviews. These contributions support the development of sustainable coastal management strategies in alignment with the United Nations (UN) Sustainable Development Goals (SDGs), specifically SDG 13 (Climate Action) and SDG 14 (Life Below Water). The specific objectives of this study are as follows: (1) to examine the coastal hydro-environmental processes addressed in the selected studies; (2) to review various methods used to assess coastal hydro-environmental processes; and (3) to identify and analyse the research trends, gaps, and challenges related to the assessment methods for coastal hydro-environmental processes.

2. Materials and Methods

This study employs a systematic review approach, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines [48], to evaluate the assessment methods used for coastal hydro-environmental processes. A bibliometric analysis was conducted on the peer-reviewed literature to examine research trends over time. Eligible studies were analysed based on their methodological focus, application context, and relevance to specific coastal hydro-environmental processes. A qualitative synthesis was then performed to provide a structured, method-specific overview of the methodologies, outlining their individual developments, applications, and limitations in coastal process assessment. Additionally, a novel process–method–time classification framework was developed to align each assessment method with the specific coastal processes it supports, offering practical insight into method suitability and underexplored research areas.

2.1. Data Collection

A systematic bibliometric analysis was conducted to identify research trends, patterns, gaps, and factors affecting the hydro-environmental processes of the coastal zone. Renowned scientific databases (i.e., SCOPUS^® and Web of Science^®) were utilised to perform the review analysis with the relevant keywords. SCOPUS^® was chosen as its content is more globally oriented and comprehensible than other databases. However, Web of Science was employed, as it provides direct links to other academic publications, which may help retrieve dependable research materials for comparison. An advanced search was performed using the same Boolean operators in the selected two online publication databases, mainly “AND” and “OR”, to search for publications with specific keywords. For SCOPUS^®, TITLE-ABS-KEY (“Keywords”) was entered for all the search strings, while for Web of Science^®, TOPIC (“keywords”) was entered within its search engine. Both databases, utilising the search engines TITLE-ABS-KEY and TOPIC, encompass all publications that contain the keywords in their titles, abstracts, and paper keywords. Integration of multiple keywords such as “coastal processes”, “shoreline processes”, “hydrodynamic”, “water quality”, “sediment transport”, “field data observation”, “physical model”, “laboratory experiment”, “numerical modelling”, “artificial intelligence”, “machine learning”, and “neural networks” were utilised in the search engine to ensure comprehensive topic coverage.

The selection criteria of the keywords were based on the PRISMA 2020 statement [48], and the search mainly focused on mapping the existing literature on the assessment methods for hydro-environmental processes in coastal zones. The inclusion and exclusion criteria for the literature selection at every stage are illustrated in Figure 1 below. For the data extraction stage, publications were screened based on several criteria. The study was conducted from 2003 to 2023, spanning two decades of development in assessment methods. This period began with the early adoption of modelling and sensing technologies in coastal research, reflecting the growing influence of interdisciplinary approaches in the field. The study was based on original research articles, review papers, and conference papers. To maintain the quality of the review, all duplications (n = 22) were thoroughly checked and filtered during the initial data cleaning process. Non-English, non-accessible, and non-relevant publications (n = 24) were excluded before proceeding to an in-depth screening process. Inaccessible publications (n = 185) were also excluded during the initial data cleaning process. During the in-depth screening process, the titles, abstracts, and keywords of each article were carefully reviewed and evaluated against predefined inclusion and exclusion criteria to ensure the quality and relevance of the academic literature. Only studies that directly addressed assessment methods, such as field data collection, modelling, laboratory experiments, or AI techniques, for evaluating coastal hydro-environmental processes, specifically hydrodynamics, sediment transport, or biogeochemical processes, were included. Review papers and publications solely on coastal management strategies (n = 56) were excluded. In addition, studies specifically on coastal geological processes (e.g., rock formation), geomorphical processes, and atmospheric (e.g., weather and climate-related) processes, which lack a focus on process-based hydro-environmental assessment (n = 39), were excluded. Similarly, papers focusing on coastal ecology, which primarily address biological interactions, and coastal microbiology, which examines microbial communities and biochemical cycles (n = 23), were considered outside the scope of this review. Figure 1 presents a comprehensive overview of the exclusion criteria for maintaining a focused scope on hydro-environmental assessment methods. As a result of this screening, the final total publications returned for review are 165, where all selected papers demonstrated clear methodological relevance in assessing hydro-environmental processes in the coastal system.

2.2. Data Analysis

This study aims to review various coastal hydro-environmental processes based on different assessment methods from a global perspective. Hence, further analysis of the publications was carried out to classify the 165 publications into the following categories: (1) publication year, (2) country of scientific production, (3) main coastal processes addressed in the articles, and (4) assessment methods of coastal hydro-environmental processes (

Table 1. Research attributes used to characterise papers on coastal hydro-environmental processes.

Research Attribute	Description	Category
Year	Publication year of the studied papers	2003–2008
		2009–2013
		2014–2018
		2019–2023
Country of Scientific Production	Total number of scientific publications produced by each country based on the institutional affiliations of all co-authors	38 countries
Coastal Processes	Coastal hydro-environmental processes of the studied papers	Hydrodynamic
		Sediment Transport
		Biogeochemical Processes
Research Focus	Assessment method of the coastal hydro-environmental processes in the studied papers	Meteorological and Hydrodynamic Data Collection
		Morphological Data Collection
		Physical Model/Experiment
		Numerical Model
		Artificial Intelligence

). These data were then analysed using multiple correspondence analysis using the package FactoMineR (Version 2.12) [49].

3. Review Findings

3.1. Publication Patterns and Foci

Based on the identified 165 publications, author affiliations were heterogeneously distributed across 38 countries. The country with the highest number of scientific publications was the United States of America (USA), with 23 publications. This is followed by China with 14; Italy with 10; France and India with 9 each; Brazil with 8; Australia, the Netherlands, and the UK with 7 each; and South Korea with 6. In contrast, the remaining 28 countries had five or fewer publications (see Figure 2). In our dataset, publications from authors affiliated with institutions in the Netherlands, the UK, Spain, and Brazil first appear from 2003 onwards, primarily focusing on evaluating hydrodynamic and sedimentation processes through field data collection and numerical modelling [50,51]. One of the earliest studies in our review to assess all hydro-environmental processes was published in 2003 by authors affiliated with institutions in Italy, Denmark, and the UK [52]. Based on our research criteria, the paradigms of the studies on coastal urban hydro-environmental processes are visually represented in varying shades of blue. Lighter shades on the map represent countries with fewer publications based on author affiliations, while darker shades indicate higher publication frequencies. Countries with no publications meeting the inclusion criteria are shown in grey (Figure 2).

Between 2003 and 2010, the number of publications was below five articles per year. More articles were published after 2010, with a notable increase in 2020, 2022, and 2023, resulting in over 15 publications per year. Based on the categorised coastal processes, investigation of biogeochemical processes which influence water quality conditions (n = 41) represents 25% of all articles, which is the highest among all coastal hydro-environmental processes. This is followed by assessing hydrodynamic (n = 35) and sediment transport (n = 35), representing 21.2% each of all articles. On the other hand, the assessment of both hydrodynamic and sediment transport represents 18.2% of studies, with 30 articles. Articles assessing all coastal hydro-environmental processes, such as hydrodynamic, sediment transport, and biogeochemical processes, represent 3.6% of all articles, comprising six articles (Figure 3). As for assessment methods, most articles implemented both field data collection and numerical modelling (n = 52) in evaluating the coastal processes, representing 31.5% of all articles. This is followed by the integration of field data collection with artificial intelligence/machine learning (AI/ML) technologies (n = 33), accounting for 20% of the total articles, with a notable number of 28 papers published between 2019 and 2023 (Figure 4). The results reveal that current assessment methods integrate numerical models and artificial intelligence tools with field data to assess coastal processes accurately.

Based on the data analysis results from FactorMineR (Version 2.12) tools, the co-occurrence network of keywords was further analysed and visualised, as shown in Figure 5 below. The visualised map showed that there were three clusters of frequent keywords. Keywords such as “hydrodynamics” and “sediment transport” were associated with keywords related to “numerical models” and “erosion” (red cluster). Keywords such as “water quality” and “environmental monitoring” were associated with keywords related to “ecosystem” and “salinity” (blue cluster). The third cluster (blue cluster) included keywords such as “remote sensing”, “artificial neural network”, and “computer model simulation” (green cluster). The presence of distinct clusters highlights the interdisciplinary nature of the field, with emerging overlaps between AI-based techniques and traditional modelling, indicating a transition toward hybrid methods.

Furthermore, Figure 6 below presents the trends in the use of different assessment methods for coastal hydro-environmental processes from 2003 to 2023, based on the yearly percentage of publications relative to the total number of studies employing each method: field data collection (n = 127), laboratory experiments (n = 15), numerical modelling (n = 100), and artificial intelligence/machine learning (AI/ML) technologies (n = 61). Field data collection showed relatively low and consistent usage until 2017, followed by a notable upward trend, reaching 15% in 2023, indicating a growing emphasis on empirical data to support process understanding. Laboratory experiment approaches exhibited a more random trend, with notable peaks in 2018, 2020, and 2021, with percentages reaching a peak of 20%. Numerical modelling has maintained a steady presence with gradual growth over the study period, reaching a peak of 14% in 2022, reflecting its ongoing importance as a core method in simulating the processes. Artificial intelligence technologies applications, in contrast, emerged later in the timeline but have grown rapidly since 2019, peaking at about 28% in 2022. This reflects increasing interest in using data-driven approaches for predictive assessments of the coastal hydro-environmental processes. Additionally, linear trendlines and R² values were used to assess the strength of the linear trends. Field data collection has shown the most consistent increase in use (R² = 0.71), followed by numerical modelling (R² = 0.61), AI/ML (R² = 0.51), and laboratory experiments (R² = 0.46). The results demonstrate a clear shift in recent years toward integrated and technology-enhanced methods, particularly those involving artificial intelligence and real-time field observations. At the same time, physical modelling remains relatively static in research adoption.

3.2. Review of Assessment Methods

In this study, a Coastal Hydro-Environmental Process–Method–Time Classification Framework (Figure 7) was introduced to systematically review and evaluate the application of various assessment methods in coastal hydro-environmental research. This framework categorises the selected literature according to the following: (i) coastal processes addressed, including coastal hydrodynamics, sediment transport, and biogeochemical processes; (ii) assessment methods used, including field data collection, laboratory experiments, numerical models, and AI techniques; and (iii) four consecutive publication periods spanning from 2003 to 2023. To clarify the coastal processes, coastal hydrodynamics refer to key physical parameters such as water level, waves, currents, and tides. Sediment transport refers to the movement of sediment driven by waves, tides, and currents, while biogeochemical processes involve the chemical and biological interactions that affect water quality in coastal areas. This classification framework offers a functional alignment between assessment methods and targeted coastal hydro-environmental processes, thereby facilitating a clear identification of emerging trends and methodological preferences within each coastal process.

According to the framework shown in Figure 7, the number of publications increases over time for most assessment methods, particularly after 2018. However, certain categories, such as experiment/physical modelling and numerical modelling for biogeochemical processes, remain relatively stable. Among all assessment methods, AI/ML techniques exhibit the most notable upward trend, with an explosive growth in research from 2019 to 2023, while field data collection demonstrates consistent growth across all processes. Field data collection remains the predominant approach throughout all processes and periods, particularly in sediment transport, which recorded 40 publications between 2019 and 2023. Numerical modelling also exhibits steady growth over periods, mainly utilised in coastal hydrodynamics and sediment transport from 2019 to 2023. The framework indicates that minimal research utilised AI/ML techniques before 2018, but has seen substantial growth since 2019, with biogeochemical processes as the primary area of application, recording 49 publications in the 2019–2023 timeframe. In contrast, laboratory experiments exhibit low usage overall, which represents a relatively minor methodological approach. In terms of specific coastal hydro-environmental processes, coastal hydrodynamics primarily relies on field data collection and numerical modelling, while AI/ML techniques remain in an emerging phase. Similarly, sediment transport exhibits a comparable pattern, although it has demonstrated a slightly higher recent adoption of AI-based approaches. As for biogeochemical processes, AI/ML techniques have experienced a significant increase since 2019.

This classification framework not only provides valuable insight into the historical development of coastal hydro-environmental research but also serves as a reference for selecting appropriate tools for future integrated studies and strategic research planning. Further method-specific bibliometric analyses, which reveal emerging trends and temporal insights across field data collection, numerical models, and AI/ML techniques from 2003 to 2023, are conducted to provide detailed evidence supporting the classification framework.

3.2.1. Meteorological and Hydrodynamic Data Collection

Data observation and collection are critical for understanding coastal processes and investigating environmental changes [20]. Various studies have emphasised the importance of collecting meteorological and hydrodynamic data, including waves, tides, currents, and bathymetry, to identify seawater circulation patterns and dynamic processes [53,54,55]. Methods for collecting meteorological data include measuring wind speed, wave characteristics, and weather conditions during field surveys in coastal areas. For hydrodynamic data collection, the deployment of autonomous oceanographic instruments involves monitoring water levels, current velocities, temperatures, and salinities in coastal areas throughout the tidal cycle [55,56,57]. These field measurements aim to provide essential data for the simulation of numerical models. Moreover, the measured field data are used for numerical model calibration and verification to verify the model’s accuracy and provide the reliability of the used model [20,36,54,55,58]. Using specialised field units and geodetic information aids in collecting and analysing data from various instruments, ensuring universality and efficiency in data collection processes [59].

Field data collection in meteorology employs various tools to obtain critical environmental measurements. Instruments such as weather sensors and anemometers enable the continuous and rapid measurement of wind characteristics and other atmospheric parameters [20,60]. Additionally, meteorological stations serve as centralised platforms that systematically record a wide range of atmospheric variables, including temperature, humidity, atmospheric pressure, precipitation, and wind patterns. These stations are strategically distributed across diverse geographical regions to provide high-resolution temporal data, which is essential for both short-term forecasting and long-term climate research [61,62]. For hydrodynamic data collection, water level, current speed, seawater quality, and sediment grab sampling are crucial in assessing the hydraulic impact on the coastal area. Data acquisition for hydrodynamic modelling involved recording temporal data points using instruments like the Acoustic Doppler Current Profiler (ADCP), Acoustic Doppler Velocimeter (ADV), current meters, tide gauges, and wave gauges, ensuring high-quality data for analysis and model calibration and verification [24,28,36,53,54,55,56,57,63,64,65]. In order to understand the physical–biogeochemical processes of the water column, parameters including salinity, turbidity, nutrients, dissolved organic carbon (DOC), total phosphorus, ammonia, chlorophyll, and microbial abundances need to be quantified. Advanced tools such as Conductivity–Temperature–Depth (CTD) devices and multi-parameter probes are often used to detect the characteristics of seawater [38,54,55,66,67,68]. Satellite-based remote sensing platforms, including the Moderate Resolution Imaging Spectroradiometer (MODIS) [69], Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) [70], and Ocean and Land Colour Instrument (OLCI) [71], were employed to estimate chlorophyll-a levels by detecting ocean colour.

Figure 8 below shows the temporal trends of meteorological and hydrodynamic field data survey instruments usage across publication periods of the selected studies. A complete list of studies corresponding to the instrument categories is also shown in Figure 8. Data without reporting direct instrument deployment and obtained from secondary sources were not categorised in the summary figure and table. According to the heatmap, offshore wind and wave measurement tools have shown moderate and distributed use across various instruments. Regarding water level and current measurement instruments, ADCP exhibits a strong upward trend, peaking at 12 publications during the 2019–2023 period, reflecting its central role in hydrodynamic studies and increasing dependence over time. The emerging tools utilised in water sampling are the Multi-Parameter Probe (YSD) and CTD devices, with eight and six publications, respectively, after 2018, indicating the growing need for integrated biogeochemical data in coastal processes studies. These trends underscore the evolving technical priorities in coastal research and reflect a growing emphasis on monitoring coastal dynamics and environmental quality over the past two decades.

Figure 8. Temporal trends of meteorological and hydrodynamic field data survey instrument usage across publication periods [20,21,23,24,26,28,36,38,51,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113].

Figure 8. Temporal trends of meteorological and hydrodynamic field data survey instrument usage across publication periods [20,21,23,24,26,28,36,38,51,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113].

3.2.2. Morphological Data Collection

Morphological data collection involves various approaches, including coastal topographic profiling [55,56], analysing sediment characteristics, and conducting bathymetry surveys to comprehensively understand sediment transport patterns and detect shoreline changes [114,115,116]. Topographic profiling involves high-resolution satellite remote sensing [27,64,114,115,116], the Differential Global Positioning System (DGPS) or the Real-Time Kinematic Global Positioning System [117,118,119,120], fixed or terrestrial Light Detection and Ranging (LiDAR) [121,122], laser scanners [37,96,115], video images for dynamic observations [40,123], and drones [124,125]. By combining in situ measurements and satellite observations, changes in shoreline and seabed profiles can be accurately detected. Technologies such as the Digital Elevation Models (DEMs) provide bare-earth topographic surfaces and enhance the quality for model calibration purposes [60,126]. As for the bathymetry survey, a single or multibeam echo sounder [55,56,114], bathymetric sonar [118,125], and LiDAR [41,99] were implemented to monitor beach profiles. These survey methods ensured accurate measurements and provided detailed insights into seabed elevation changes and sandbar dynamics for better shoreline management planning.

Moreover, seawater turbidity and suspended solid concentrations can be evaluated using optical backscatter sensors (OBSs) by measuring infrared radiation scattered from suspended matter in seawater [23,36,93]. The Van Veen grab sampler [98] or sediment corer [127] was then employed to collect sediment samples for laboratory tests and particle size distribution analysis. By combining field measurements with advanced modelling techniques, researchers can gain insights into the complex morphological interactions shaping coastal areas and their implications for coastal environments. Figure 9 illustrates the temporal trends of the commonly used field data survey instruments for collecting morphological data across the selected studies, as reported in the publication years. A complete list of studies corresponding to the instrument categories is also shown in Figure 9. Data without reporting direct instrument deployment were not categorised in the summary figure and table. Among the four bathymetry survey equipment, echo sounders show consistent usage, with five publications during 2014–2018 and three publications in the most recent period (2019–2023). A clear shift is observed after 2018, from basic GPS-based methods to more advanced technologies such as sonar and LiDAR. In beach morphology surveys, there is a transition from manual and positioning-based methods toward remote sensing and advanced aerial technologies, with growing interest in satellite imagery (12 publications in total), laser scanning (4 publications), and drone-based mapping (2 publications). Meanwhile, sediment grab sampling equipment remains moderately used and is distributed across various instruments. These trends reflect a broader shift in traditional morphological data collection toward higher-resolution, remote, and automated survey technologies in response to growing data demands and the need for greater field efficiency.

Figure 9. Temporal trends of morphological data survey instrument usage across publication periods [23,25,26,27,36,37,40,41,52,53,55,56,60,63,64,76,79,80,81,82,93,94,95,96,97,98,99,100,101,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144].

Figure 9. Temporal trends of morphological data survey instrument usage across publication periods [23,25,26,27,36,37,40,41,52,53,55,56,60,63,64,76,79,80,81,82,93,94,95,96,97,98,99,100,101,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144].

3.2.3. Laboratory Experiments/Physical Model

Physical models in laboratory settings offer controlled environments for studying coastal processes such as hydrodynamics, accretion, and erosion of the coastal area. Researchers employ various scales of physical models, from small-scale flumes to large wave basins, to simulate and analyse hydro-environmental phenomena. Numerous studies [58,77,96,145] delved into sediment transport dynamics, analysing the movement of sediments under various wave and current conditions through laboratory experiments. Experimental test data were used to validate the numerical model [58] and the modified fundamental equation [146] to predict the erosion rate of the coastal area. Laboratory experiments also focus on the interaction between waves and coastal structures. However, achieving accurate scale modelling in laboratory experiments is a critical consideration. The literature from Welzel et al. emphasised challenges related to conducting physical model tests using a jacket-type model in the 3D wave and current basin to assess the erosion patterns around a hydrodynamic transparent offshore foundation exposed to combined waves and currents [96]. Laboratory experiments also simulated storm surge events and coastal flooding scenarios to assess the physical processes of dissipative, intermediate, and reflective sandy beaches under storm wave events [147]. Parameters can be estimated through wave flume experiments to derive an analytical model for determining the sediment setting velocity from available field data [54]. Moreover, the depositional environment and sediment transport direction were determined from the average grain size, standard deviation, skewness, and kurtosis. Therefore, laboratory tests on sediment characteristics were carried out and processed on GRADISTAT [77].

Through physical models, an understanding of tidal and hydrodynamics will also be enhanced, providing continuous spatiotemporal data of better overall quality [29,148]. Therefore, a laboratory experiment basin test case was utilised to evaluate the capability of a neural network model in predicting nearshore dynamics [148]. Aquatic organisms such as seagrass, kelp, and mangroves tend to form submerged canopies, which modify the local wave-driven hydrodynamics within coastal and estuarine regions. Thus, studies from van Rooijen et al. provided new insight into the mechanisms of wave-driven mean flows within submerged canopies and guidance for how this hydrodynamics can be predicted in coastal wave–circulation models [28]. Furthermore, the understanding of biogeochemical processes in coastal systems can be enriched through experiments. A study by Cluzard et al. implemented laboratory microcosm studies to determine the influence of sediment-associated microplastics on ammonium cycling within intertidal sediments [127]. Through mesocosm experiments in an annular flume, Grasso et al. investigated organism-driven sediment transformation and deposition under simulated current and wave conditions [149]. Another study from Southwell et al. focused on biogeochemical cycling of nutrients through photochemical processes, which may affect primary productivity and nutrient dynamics in coastal zones [150].

3.2.4. Numerical Models

Numerical modelling plays a crucial role in assessing coastal hydro-environmental processes by simulating various factors affecting the coastal environment. Hydrodynamic modelling aids in simulating the water level variations and current velocities induced by forcing functions such as tides [31,65], while wave modelling computes wave propagation, wave generation by wind, and dissipation for a given bottom topography, wind field, water level, and current field in deep water, where both models can provide insights into coastal flow patterns [36,100,139,151]. Moreover, sediment transport modelling simulates the deposition and erosion of the existing seabed materials. It predicts the impact of natural developments and artificial activities on coastal morphology, emphasising the importance of precise sediment transport models for reliable morphodynamic predictions [35,37,90,152]. As for fine materials with low settling rates that are easily disturbed, sediment plume dispersion studies were carried out through numerical modelling [54,153]. As for biogeochemical modelling, it simulates the interactions between water movement, nutrient dynamics, and biological processes in aquatic systems [27,59,64]. Accurate prediction of water quality outcomes in coastal areas can be achieved by applying coupled hydrodynamic–biogeochemical modelling systems [61,62,87,102]. Model setup for all modelling conditions involves nested models of different domains with varying resolutions and time steps to accurately simulate all concerned parameters of coastal hydro-environmental processes [38,154]. These numerical models allow for calibration and validation by comparing measured field data records with detailed simulated data to further enhance the accuracy and feasibility of the collected data [20,21,37,64]. By combining monitoring and experimental data with numerical models, researchers can better understand coastal hydro-environmental processes, predict their impact on the coastal environment, and improve the accuracy of simulations for different modelling conditions [29,58].

Various numerical modelling tools have been utilised in the selected studies for coastal hydro-environmental modelling. More than 20 numerical models have been discussed in the selected studies, as different numerical models are utilised explicitly for different aspects, incorporating factors like bathymetry, wave data, sediment characteristics, and calibration parameters. Standard numerical modelling tools that assess all coastal hydro-environmental processes, including hydrodynamic, sediment transport, and biogeochemical processes, are DELFT3D, MIKE 21/3, the 3D hydrodynamic Finite Volume Coastal Ocean Model (FVCOM), the Semi-Implicit Cross-Scale Hydroscience Integrated System Model (SCHISM), and the Regional Ocean Modelling System (ROMS). Numerous studies have developed a numerical model using Delft3D to simulate coastal circulations, dynamics, and wave transformation processes in the research coastal environments, including estuaries, tidal marshes, and river deltas by utilising survey data for accurate simulations and model calibration and verification [21,24,55,155]. Morphodynamics, erosion, and sediment patterns influenced by waves and storm activities were also assessed using Delft3D to evaluate their impact on coastal environments [23,63,82,152]. Furthermore, Delft3D was also utilised to simulate coastal compound flooding along the southeast Atlantic coast of the U.S. by integrating remote sensing techniques for flood mapping in the coastal regions [41]. To assess biogeochemical processes, a study by Abayazid and El-Adawy successfully implemented and validated the Delft3D model to evaluate water quality parameters in the deltaic coastal lake in Egypt by combining remote sensing techniques [27]. These studies demonstrated the versatility and effectiveness of Delft3D in assessing various coastal hydro-environmental processes. However, some studies have utilised the MIKE model to simulate coastal hydrodynamic processes and compare them with collected field data for further model calibration [20,31,156,157]. Shoreline evolution and sediment transport assessment were also identified using the MIKE model for better coastal management planning [35,37,117,158]. Furthermore, Flindt et al. implemented the MIKE model to assess eelgrass recovery in Danish estuaries using GIS tools for field monitoring [143]. Moreover, FVCOM was developed to determine estuarine circulation patterns and sediment dynamics by implementing sediment budget analysis under varying flow conditions in Fitzroy Estuary–Keppel Bay [38]. FVCOM was also utilised to determine water quality parameters in Mass Bay by assessing physical–biogeochemical processes in the coastal regions [158].

Another advanced hydrodynamic and biogeochemical modelling system, SCHISM, was implemented to investigate the driving mechanisms of the Zhe-Min Coastal Current in the East China Sea, providing valuable insights into the regional circulation patterns and associated nutrient transport dynamics [159]. SCHISM was also integrated with the Integrated Compartment Model (ICM) to simulate high-frequency dissolved oxygen dynamics in shallow estuarine systems, capturing the interactions between physical transport processes and biological mechanisms such as photosynthesis, respiration, and sediment fluxes [160]. Another implementation of the ROMS coupled with a biogeochemical model was used to simulate the influence of transient wind-driven upwelling and mesoscale eddies on nutrient fluxes and phytoplankton productivity along the Ningaloo Reef, highlighting the critical role of physical forcing in shaping biological responses in coastal upwelling systems [87]. Furthermore, studies by Deb and Chakraborty developed a three-dimensional coupled hydrodynamic–biogeochemical–sediment transport model based on ROMS to evaluate the influence of tidal dynamics and river discharge on chlorophyll-a distribution and estuarine productivity in the Hooghly Estuary [62]. The study demonstrated that tidal forcing and suspended sediments significantly modulate nutrient availability and primary production [62]. Other numerical models utilised in the selected studies, such as SWASH, SWAN, and others, are arranged and organised as a heatmap in Figure 10 based on their ability to assess specific coastal hydro-environmental processes. These numerical models tend to validate real-world scenarios by simulating wave behaviour, sedimentation patterns, and hydrodynamics under various conditions. Hence, numerical modelling enables researchers to better understand current coastal conditions to avoid potential natural disasters and impacts on coastal areas through immediate mitigation measures.

Figure 10. Temporal trends of numerical model usage in coastal processes studies across publication periods [20,21,23,24,27,28,29,30,31,35,36,37,38,41,47,54,55,58,59,61,62,63,64,65,66,70,72,73,75,77,79,81,82,83,86,87,90,91,92,98,100,102,105,113,114,117,131,139,143,144,148,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179].

Figure 10. Temporal trends of numerical model usage in coastal processes studies across publication periods [20,21,23,24,27,28,29,30,31,35,36,37,38,41,47,54,55,58,59,61,62,63,64,65,66,70,72,73,75,77,79,81,82,83,86,87,90,91,92,98,100,102,105,113,114,117,131,139,143,144,148,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179].

Figure 10 illustrates the temporal trends of numerical model usage in the selected studies across publication periods, with a detailed analysis of the three coastal process domains: coastal hydrodynamic processes, sediment transport, and biogeochemical processes. A complete list of studies corresponding to the numerical models is also shown in Figure 10. From the heatmap, the coastal hydrodynamic processes domain has widely adopted models like DELFT3D (six publications), MIKE 21/3 (four publications), SWASH (three publications), and ROMS (three publications) in simulating wave–current interactions, water levels, and tide propagation from 2019 to 2023. Sediment transport studies, which simulate the movement of sediment driven by waves, tides, and currents, maintain reliance on multi-capability models like DELFT 3D and MIKE 21/3, with Xbeach and CMS as emerging tools in recent periods (2019–2023). The usage of models in biogeochemical processes studies shows a variety of specialised tools used for simulating nutrient transport, algal blooms, and hydrodynamic–biogeochemical coupling that influence water quality. These trends indicate that numerical modelling in coastal studies is moving toward more specialisation, integration, and application-specific use, due to the advances in computational capabilities and the increasing demand for multi-disciplinary data analysis.

3.2.5. Artificial Intelligence

Integrating field data, numerical models, and artificial intelligence tends to improve the prediction of coastal hydro-environmental processes and assess the long-term impacts on the coastal environments. Numerous studies have utilised machine learning techniques, neural networks, and deep learning to enhance the simulation and prediction of coastal hydrodynamic and morphodynamic processes. Machine learning tools, such as Gaussian Process Regression, provide an accurate and probabilistic estimation of coastal circulation patterns, sedimentation, erosion, and water quality conditions based on various factors [69,135]. Gaussian process predictors were utilised to accurately predict hourly wave runup elevation with uncertainty estimation [129]. Hourly samples of wave runup data collected by a fixed LiDAR at Narrabeen Beach, Sydney, Australia, were used to evaluate the performance of the Gaussian process runup predictor [129]. The Gaussian process runup predictor was tested on these unseen data samples, achieving well-performing outcomes with a root-mean-squared error (RMSE) of 0.18 m [129]. Moreover, a new bathymetry estimation approach was introduced, combining a physical wave model with a statistical method based on Gaussian Process Regression, to predict coastal morphological changes when in situ data are unavailable [135]. The GPR algorithm was also implemented to predict uncertainties in the water quality index models at the sampling site [43]. Machine learning tools that aid in predicting both morphological changes and biogeochemical processes in coastal areas include the Support Vector Machine (SVM), Random Forest Model, Support Vector Regression (SVR), and Decision Trees. An SVM classifier was utilised for coastal area detection in Gangwon Province, South Korea [132]. Furthermore, the performance of the ANN and SVM was compared to forecast the trend and magnitude of algal growth in Tolo Harbour, where the performance of the SVM is better than all ANN models in terms of water quality prediction results, but with lower computational efficiency [180]. As for the Random Forest Model, studies have shown that it can predict seawater quality parameters accurately based on the suspended solid concentrations [68,181,182,183]. Support Vector Regression and Decision Trees were also utilised in some studies to compare the performance of the machine learning tools [68,183,184].

Regarding Neural Networks, most studies have utilised ANNs for wave hindcasting, shoreline evolution, and water quality prediction. In addition, Mixture Density Networks (MDNs), Backpropagation Neural Networks (BPNNs), Recurrent Neural Networks (RNNs), and Self-Organising Maps (SOMs) have been implemented for water quality forecasting. For example, ANN models were applied for sea level prediction by correcting errors in hydrodynamic model predictions [173]. The results of this study showed that the neural networks improve sea level prediction accuracy by 50% while the combined approach reduces sea level prediction errors by 20–30%. Furthermore, ANN models were implemented for wave climate hindcasting and current characteristic estimation [85,155], showing good generalisation ability and high correlation with numerical models, enabling faster reconstruction of long-term wave and current data. The ANN model was also outperformed, achieving a 4% MAPE in sedimentation prediction [47]. Studies have shown that the predictions of water quality parameters such as Chl-a, ammonia concentration, suspended solids, and turbidity by implementing ANN models were outperformed too [111,112,185,186,187,188]. Regarding Mixture Density Networks, the MDN model exhibited negligible biases and moderate uncertainties for water quality indicators, outperforming Chl-a retrievals significantly [110,111]. On the other hand, some studies also indicated that BPNNs were better at predicting bloom magnitude as they tend to produce reliable results with high correlation coefficients and low errors [136,184].

Furthermore, deep learning models aid in mapping wave runup on beaches, providing insights into wave hydrodynamics and coastal risk assessment by predicting tidal-driven currents, wave propagation, and wave breaking [31,148]. Moreover, a study by Muñoz et al. analysed compound flooding dynamics using CNN and achieved 97% overall accuracy in flood mapping [41]. For shoreline evolution aspects, these deep learning models can emulate the results of numerical models, optimising the use of computational resources. For example, a convolutional neural network CNN-based semantic segmentation model was used for analysing beach imagery to reproduce erosion and sedimentation patterns with a high accuracy of 95.1% precision [123]. Meanwhile, a study has shown that the CNN model achieved 75% to 96% accuracy in classifying coastal images and distinguishing coastal features [40]. On the other hand, a study by Fei et al. utilised LongShort-Term Memory (LSTM) to integrate with a physics-based model for water level prediction [172]. Another study by Latif et al. has shown that LSTM also outperformed other models for sediment transport prediction with RMSE of 11.395, MAE of 18.094, and R2 of 0.914, demonstrating its effectiveness in predicting morphological changes [57]. LSTM can also be implemented to predict algal growth [184,189]. Artificial intelligence tools offer quick responses, accurate identification of significant variables, and robustness in learning complicated relationships, making the integrated AI model a valuable tool for predicting and managing coastal hydro-environmental processes.

Figure 11 shows the temporal trends of artificial intelligence tool usage in the selected studies across publication periods, with detailed analysis across the three coastal processes domains: coastal hydrodynamic processes, sediment transport, and biogeochemical processes. A complete list of studies corresponding to artificial intelligence tools is also shown in Figure 11. From the figure, the adoption of AI tools in all three coastal processes studies was low before 2014. The dominant tools in assessing coastal hydrodynamic processes are CNN, GPR, and ANN in recent periods (2019–2023). Sediment transport studies demonstrate an emerging trend in the application of AI tools, especially with the use of ANN (six publications), CNN (four publications), and Linear/Logistic Regression (four publications) from 2019 to 2023, reflecting the increasing analytical demands of sediment dynamics research. In the biogeochemical process domain, a wider diversity of AI tools has been utilised to assess complex water quality interactions in recent periods (2019–2023). The dominant tools are the Random Forest Model (nine publications), Support Vector Regression (five publications), and ANN (five publications). These trends reveal that AI tool preferences vary depending on the complexity and data characteristics of specific coastal processes. These advancements highlight the significance of artificial intelligence in enhancing the understanding and management of coastal environments, offering beneficial tools for sustainable coastal development and environmental protection.

Figure 11. Temporal trends of artificial intelligence tool usage in coastal processes studies across publication periods [22,31,40,41,42,43,45,47,57,66,68,69,71,75,78,79,81,85,104,106,108,110,111,112,116,119,123,124,129,132,134,135,136,137,138,148,152,154,157,162,163,166,172,173,180,181,182,183,184,185,186,187,188,189,190,191].

Figure 11. Temporal trends of artificial intelligence tool usage in coastal processes studies across publication periods [22,31,40,41,42,43,45,47,57,66,68,69,71,75,78,79,81,85,104,106,108,110,111,112,116,119,123,124,129,132,134,135,136,137,138,148,152,154,157,162,163,166,172,173,180,181,182,183,184,185,186,187,188,189,190,191].

4. Research Gaps, Challenges, and Future Recommendations

This section outlines key research gaps, challenges, and strategic recommendations across four domains: field data collection, laboratory experiments, numerical modelling, and artificial intelligence (AI) integration for assessing coastal hydro-environmental processes. Conventional methods and modern technologies implemented during the field data collection stage were discussed in Section 3.2.1 and Section 3.2.2 above. For traditional methods during the field data collection stage, there were common challenges such as expensive field survey equipment [85], inadequate maintenance of hydrological infrastructure leading to discrepancies in measurement data [24,100,101,114], restricted access to coastal areas [60], and the inability to conduct field surveys during the complex hydrodynamic conditions and extreme weather conditions [74,107,123]. Furthermore, most studies have only conducted short-term field campaigns, resulting in limited long-term insights and imprecise data estimation [60]. These surveys can provide thorough sampling data but are limited in terms of spatial and temporal coverage [192]. Therefore, modern methods such as remote sensing techniques, video cameras, and satellite monitoring are recommended to provide broader spatial coverage. Studies by Elsner et al. have demonstrated the strengths and limitations of various surveying techniques, including UAS, vehicle-mounted, and airborne LiDAR, emphasising that surface type heterogeneity can significantly influence elevation accuracy in coastal environments [193]. However, these methods faced challenges in capturing fine-scale data due to their low-resolution spatial and temporal data, which require further calibration [84,136,140,141]. A comparison of traditional and advanced methods utilised in the field data collection stage is tabulated in

Table 2. Comparison of conventional methods and modern technologies in the field data collection stage.

Aspect	Conventional Methods	Modern Technologies
Data Collection	Manual field measurements (e.g., tide gauges)	Remote sensing (UAS, satellite, LiDAR), real-time sensors
Scale of Assessment	Local, small-scale	Regional to global scale through satellite data
Resolution	Low resolution, limited to surface observations	High-resolution (3D data, detailed bathymetry)
Computation	Simple empirical models or equations	Advanced numerical simulations, AI, machine learning-based forecasting
Accuracy	Moderate, prone to human Error and environmental limitations	High-accuracy, automated systems reduce uncertainties and support dynamic environments
Predictive Capabilities	Short-term predictions, localised focus	Long-term, multi-scenario modelling with real-time adaptability
Cost and Time	Labour-intensive, expensive, time-consuming	Efficient, automated, scalable

below. To address the identified gaps and challenges, future recommendations include expanding the implementation of long-term monitoring programmes, especially in regions where such efforts are limited; promoting consistent sampling to improve numerical model performance; developing automated field data acquisition processes; and integrating multiple sensors with advanced sensing technologies to enhance coastal monitoring capabilities and support broader-scale, globally relevant analyses [106].

Compared to full-scale field measurements or high-resolution numerical simulations, physical laboratory experiments typically require fewer data points and less intensive post-processing, resulting in more controlled and reliable datasets. However, laboratory experiments cannot fully replicate the complete representation of the coastal environment due to the complexity of natural coastal conditions and processes [120]. Furthermore, models that are disproportionately scaled compared to field conditions in laboratory experiments can lead to inaccuracies and distortions of real-world coastal dynamics [96]. Hence, combining approaches by integrating field data, laboratory data, and numerical models is recommended to validate the model across various scales and conditions.

Moreover, the most common research gaps and challenges encountered in numerical modelling include the high computational costs [154,155,162], the complexity of various numerical models [151], and the simulation time of the model [179]. Numerical simulations of hydrodynamics, sediment transport, and biogeochemical processes are computationally intensive, especially when high spatial-temporal resolution and long-term scenario modelling are required. The complexity increases further when integrating multiple processes or coupling with AI models for prediction and optimisation. Hence, limited access to high-performance computing infrastructure can be a significant issue. In addition, some studies also discussed challenges in accurately simulating complex physical processes, including ensuring tracer dynamics consistency with varying hydrodynamic and transport time-step sizes [10,66,144]. Model uncertainties due to parameterisation and grid discretisation led to inaccurate results that do not align with real-world scenarios [24,36,41,68,114,117]. For example, Mulligan et al. stated that the model grid resolution has limited the resolution of intertidal channels [23], whereas challenges faced by Warner et al. included resolving subgrid-scale processes with vertical grid spacing limitations [86]. Due to missing data in some studies, simplified assumptions and boundary conditions were implemented in their numerical models [38,73,114,117]. To address these challenges, broader applications across various environmental backgrounds must be explored. This investigation should encompass the identification and application of potential resources, including the interactions between ecological systems and coastal processes that can be integrated into the model [194,195,196,197]. Moreover, relevant field observations should be conducted to refine model parameters for better predictions. Integrate models with real-time data and AI techniques with advanced algorithms to enhance the precision in predicting coastal hydro-environmental processes. Numerical models must also be developed to better incorporate climate change scenarios [38,117], including sea level rise and increased storm surge intensity, ensuring simulations reflect future conditions rather than relying solely on historical baselines.

Nevertheless, integrating numerical models with real-time field or experimental data and AI techniques presents gaps and challenges. The accuracy of the AI-integrated model depends on the quality and quantity of the training data; however, most studies lack long-term, real-time data [57,119]. Therefore, it is challenging to optimise parameters related to coastal hydro-environmental processes with limited data to enhance coastal area management [119,181]. Furthermore, balancing the computational efficiency and accuracy of the AI-integrated models is also challenging [152]. Moreover, the study by Petropoulos et al. faced issues in validating new image processing algorithms against traditional methods due to the limited exploration of SVMs in coastal mapping applications [134]. Therefore, consideration of different algorithms, machine learning tools, and additional input variables is essential to obtain the most consistent results [68,180,184]. Data standardisation [186] and universal algorithms [124] also significantly impact the performance of AI-integrated models. Hence, model generalisation should be improved to develop advanced hybrid models combining different methods with real-time data. For example, approaches like the Random Walk Method (RWM) demonstrate that simplified but physically consistent models can be combined with data-driven techniques to reduce computational demand without sacrificing accuracy [198].

Advancing the assessment of coastal hydro-environmental processes requires a multidisciplinary and integrated approach. This study presents a detailed synthesis of four core assessment methods and incorporates a systematic bibliometric analysis that quantitatively reveals emerging integration trends across these domains. In contrast to the existing literature that predominantly focuses on a single method or specific coastal processes, this review comprehensively evaluates all major assessment methods across key coastal hydro-environmental processes by presenting a process-method categorisation framework, facilitating functional linkage between assessment methods and affected coastal processes. This combined perspective highlights the need for hybrid models and supports the development of methodologically diverse yet functionally connected strategies for sustainable coastal management. Hence, future research should focus on improving hybrid frameworks, as well as high-quality databases and computational tools, to address the growing challenges posed by dynamic and climate-affected coastal processes.

5. Conclusions

To achieve effective and sustainable coastal management, it is essential to comprehend coastal hydro-environment conditions through various assessment methods. This review synthesised 165 peer-reviewed articles to evaluate the key research trends and challenges related to methods in assessing coastal hydro-environmental processes in terms of hydrodynamic, sediment transport, and biogeochemical dynamics. Through a combined bibliometric and qualitative synthesis, this study presents a method-specific overview of techniques in field surveying, experimental models, numerical modelling, and AI-based approaches, highlighting their relative developments, applications, and constraints. The findings reveal a clear transition from individual methodologies to integrated, multidisciplinary approaches, reflecting increasing emphasis on predictive accuracy and real-time data processing.

Developments in field measurement, remote sensing, numerical modelling, and artificial intelligence (AI) improve predictive capabilities of the complex coastal hydro-environmental processes, emphasising the necessity for continued research and innovation in this coastal field. Despite these advancements, issues such as model accuracy, limited long-term datasets, complicated model parameterisation, and high computational and surveying expenditures still remain. Hence, future research should emphasise the development of hybrid models that integrate artificial intelligence (AI), numerical simulations, and in situ field measurement in coastal hydro-environmental process assessment. These models need to improve their predictive capabilities while maintaining computational efficiency by implementing real-time, high-resolution input to better represent evolving coastal dynamics, even under climate change scenarios. Employing non-traditional data sources, such as remote sensing, and developing standardised frameworks for data integration, model validation, and uncertainty analysis will further advance forecasting and model interoperability. By linking methodological disciplines, hybrid models can be powerful tools for early warning systems, scenario simulations, and adaptive coastal management, leading to more resilient, evidence-based responses to coastal environmental challenges. Collaboration among scientists, policymakers, and communities is crucial to co-developing accessible tools and establishing more resilient coastal management strategies. Policymakers should invest in long-term monitoring facilities and promote cross-sectoral collaboration to enhance the effectiveness of their policies. These efforts will strengthen innovation capacity and directly contribute to achieving SDG 13 (Climate Action) and SDG 14 (Life Below Water), thereby facilitating sustainable coastal governance.