Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era

Topic Information

Dear Colleagues,

Natural hazards such as floods, landslides, droughts, volcanic eruptions, forest fires, and earthquakes have always affected the Earth and human life, shaping the evolution of our planet and life. In recent years, climate change coupled with the growing and not homogeneously distributed anthropic pressure have increased the severity and frequencies of natural hazards (NHs). NHs thus require innovative ways of predicting and mitigating their occurrence in order to save vulnerable communities who are at risk. The management of natural hazard risks requires a holistic approach, integrating different methodologies and data to ensure a comprehensive overview of the entire phenomena in all its phases. Ground information, drones, satellites, and modelled data should be combined and integrated by using different approaches/technologies in attempt to enhance their main advantages of providing reliable and continuous information.

In this regard, Decision Support Systems (DSSs) can improve decision-making and provide support to identify areas of high risk. However, classic DSSs commonly rely on static models and pre-established rules that do not adapt to the complex and dynamic natural environments of today. Thus, it is paramount to monitor and assess NHs for risk management and better provide DSSs for risk mitigation and minimizing infrastructure losses arising from these NHs. DSSs in the age of AI can generate actionable insights and better understand the natural hazard risk-prone areas by applying machine learning or deep learning models to process large volumes of different kinds of data and recognize patterns. The field of natural hazards is considered to be one where AI can save human lives and losses by better predicting natural hazards, enabling timely preparedness and, when they happen, provide mitigation strategies to better manage them. In addition to managing natural hazards, several other applications of these AI techniques can be used, such as predicting structural vulnerability to seismic events, landslides, and flood risk management and monitoring. The use of innovative technologies, such as digital twins, AI, and DSSs, can support natural risk assessment at the spatial level but also within the urban environment. Consequently, it can support urban planning choices and governance as a result of scenario assessments and data-driven predictive models in order to make more sustainable choices.

The integration of AI with digital twin improves the analytical and operational capabilities of geospatial systems, which through the analysis of historical data and the integration of real-time information (IoT) are able to highlight even “hidden patterns” in the data, identifying new models capable of improving forecasts with greater control over the quantification of uncertainty and the variability of the phenomenon analysed.

This Topic aims to focus on natural hazard assessment, monitoring, and management to understand the risks of natural hazards, such as floods, landslides, droughts, and seismic events. This Topic invites the submission of articles focused on, but not limited to, the following areas:

• Monitoring of natural hazards for risk assessment and communication.
• Digital twins (DTs)/prototypes of DTs in natural hazard forecasting, early warning, monitoring, and supporting tools for urban governance.
• DSSs to extract meaningful information in the artificial intelligence era, eventually serving to reduce risk and provide support tools to mitigate natural hazards.
• The role of AI and digital twins to assess the economic impacts of natural hazards and the cost-effectiveness of various mitigation strategies.
• Novel techniques to analyse big data coming from Earth observation platforms, drones, and other geospatial data in order to provide timely information related to the extend, exposure, and impacts of natural hazards.

Dr. Raffaele Albano
Dr. Teodosio Lacava
Dr. Antonietta Varasano
Dr. Ida Giulia Presta
Dr. Mayank Mishra
Dr. Meriam Lahsaini
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800
Hydrology hydrology	3.1	6.0	2014	16.5 Days	CHF 1800
ISPRS International Journal of Geo-Information ijgi	3.2	6.7	2012	34.9 Days	CHF 1900
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700
Smart Cities smartcities	6.6	13.0	2018	25.1 Days	CHF 2000

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Published Papers (8 papers)

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29 pages, 17032 KB  

Open AccessArticle

A Geospatial Framework for Landslide Risk Assessment of Road Infrastructure at a Regional Level in Greece

by Zoe Misiri, Alkistis Antonopoulou, Nikolaos Depountis, Panagiotis Ioannidis and Andreas Kazantzidis

ISPRS Int. J. Geo-Inf. 2026, 15(6), 246; https://doi.org/10.3390/ijgi15060246 - 2 Jun 2026

Viewed by 441

Abstract

This study presents a geospatial framework for assessing landslide risk along one of the most landslide-prone road networks in Greece, located in the Region of Epirus. Utilizing a field-verified inventory of 295 active landslides, the research evaluates five key predisposing factors (lithology, slope, [...] Read more.

This study presents a geospatial framework for assessing landslide risk along one of the most landslide-prone road networks in Greece, located in the Region of Epirus. Utilizing a field-verified inventory of 295 active landslides, the research evaluates five key predisposing factors (lithology, slope, elevation, land use, and cumulative annual precipitation) using the bivariate Frequency Ratio (FR) statistical model. Among six tested configurations, the baseline model integrating all factors demonstrated the highest reliability, quantitatively validated through Prediction Rate Curves yielding an Area Under the Curve (AUC) of 0.788 with the use of an independent dataset of 126 landslides. As a spatial outcome of this statistically validated configuration, nearly 80% of the study area was classified within Moderate to Very High susceptibility zones. The resulting Landslide Susceptibility Index (LSI) was converted into an event-based Landslide Hazard Index (LHI) and integrated with a weighted Road Vulnerability Map based on functional importance and traffic volume. The final Landslide Risk Map highlights critical risk clusters along major transportation corridors traversing weak geological formations, steep slopes, and high-precipitation areas. This quantitative approach provides a focused decision-support tool for regional authorities to prioritize geotechnical monitoring and allocate resources for road infrastructure improvement and safety. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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67 pages, 3540 KB  

Open AccessReview

When Hazard Maps Are Not Predictions: A Critical Assessment of MCDA in Glacier Hazard Susceptibility

by Ricardo Gacitua, Javier Pereira, Hernán Astudillo, Carla Taramasco and Pedro Contreras

ISPRS Int. J. Geo-Inf. 2026, 15(6), 245; https://doi.org/10.3390/ijgi15060245 - 1 Jun 2026

Viewed by 766

Abstract

Background: Multi-criteria decision analysis (MCDA) has become a dominant approach for glacier hazard susceptibility mapping, widely used to support risk management and climate adaptation planning. However, despite its widespread adoption, the role of MCDA outputs remains conceptually ambiguous: hazard classifications are often [...] Read more.

Background: Multi-criteria decision analysis (MCDA) has become a dominant approach for glacier hazard susceptibility mapping, widely used to support risk management and climate adaptation planning. However, despite its widespread adoption, the role of MCDA outputs remains conceptually ambiguous: hazard classifications are often interpreted as predictive representations of risk, even though they are derived from preference-dependent decision models. This raises a critical but underexamined question regarding the reliability of MCDA-based glacier hazard assessments. This issue becomes particularly relevant in the current transition toward data-driven and artificial intelligence (AI)-based approaches for hazard modelling, where similar challenges of interpretability, validation, and reliability arise. Methods: To address this issue, we conducted a systematic literature review following the PRISMA 2020 protocol, analysing peer-reviewed studies published between 2015 and 2025. After screening 571 records, 60 studies were included. Data were extracted using a structured framework and synthesised through quantitative descriptive analysis and qualitative assessment of modelling practices, including method selection, criteria weighting, uncertainty treatment, validation, and geographical distribution. This study conducts a structured methodological audit—not a catalogue—of multi-criteria decision analysis (MCDA) applications in glacier hazard susceptibility mapping. Results: The analysis reveals a consistent methodological pattern. The Analytic Hierarchy Process (AHP) dominates current practice (36/60 studies, 60%), typically implemented through GIS-based weighted overlay with expert-derived weights. Critically, 80% of studies (48/60) derive criteria weights exclusively from expert judgement, with no data-driven calibration or sensitivity testing of subjective inputs. This epistemic reliance on unstructured or semi-structured expert elicitation, presented without robustness analysis, forms a central concern of this review. Moreover, empirical validation is limited: only 21/60 studies (35.0%) report quantitative performance metrics. Uncertainty and robustness analyses are rarely conducted, and most studies rely on single-model configurations without comparative evaluation. Despite these limitations, the resulting hazard maps are frequently presented as objective spatial predictions. The evidence base is also geographically concentrated, with 48/60 studies (80.0%) located in High Mountain Asia. Conclusions: The findings indicate a systematic mismatch between how MCDA-based hazard maps are constructed and how they are interpreted. In most cases, MCDA functions as a decision-structuring framework rather than a validated predictive model, yet its outputs are commonly treated as predictive evidence. This gap has important implications for the use of such models in risk management and climate adaptation, particularly in the emerging context of AI-driven hazard modelling, where issues of model validation, interpretability, and reliability become even more critical. Advancing the field requires explicit validation against observed events, systematic robustness and sensitivity analysis, transparent uncertainty modelling, and comparative evaluation of alternative or hybrid decision frameworks. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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25 pages, 11171 KB  

Open AccessArticle

Multilevel Flood Susceptibility Mapping by Fuzzy Sets, Analytical Hierarchy Process, Weighted Linear Combination and Random Forest

by Pece V. Gorsevski and Ivica Milevski

ISPRS Int. J. Geo-Inf. 2026, 15(4), 148; https://doi.org/10.3390/ijgi15040148 - 1 Apr 2026

Viewed by 1753

Abstract

Given the increasing frequency and intensity of floods, which are mostly caused by continuous climate change and growing human pressures on the environment, accurately identifying areas that are susceptible to flooding is a crucial priority for risk reduction and long-term land use planning. [...] Read more.

Given the increasing frequency and intensity of floods, which are mostly caused by continuous climate change and growing human pressures on the environment, accurately identifying areas that are susceptible to flooding is a crucial priority for risk reduction and long-term land use planning. Thus, this research examines multilevel flood susceptibility mapping across North Macedonia, using 328 past flood occurrences, 14 conditioning variables derived from a digital elevation model, simplified lithology, and calculated direct runoff. The methodology integrates fuzzy set theory (Fuzzy), analytic hierarchy process (AHP), weighted linear combination (WLC), and random forest (RF) approaches. The two-stage process employs distinct sets of conditioning factors in sequential flood susceptibility mapping: first, generating Fuzzy/AHP/WLC predictions and pseudo-absence data, and second, producing five RF predictions by varying pseudo-absences and binary cutoffs. Validation results indicate that the very high susceptibility class (0.8–1.0) of the Fuzzy/AHP/WLC model predicted 46.6% of flood pixels within 31.6% of the total area. In comparison, the very high susceptibility class of the RF models predicted 88.5%, 78.3%, 60.6%, 48.5%, and 28.3% of flood pixels within 54.7%, 42.2%, 30.5%, 27.0%, and 25.1% of the total area, respectively. The RF models achieved area under the curve (AUC) values exceeding 0.850, with a maximum of 0.966. Additionally, areas of high and low uncertainty were highlighted using a standard deviation map created from the RF models, highlighting agreement/disagreement and potential locations for methodological improvement and focused sampling. The findings also highlight the potential of the multilevel technique for mapping flood susceptibility and call for more research into its potential for future studies and practical uses. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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25 pages, 10373 KB  

Open AccessArticle

Ionospheric Scintillation Anomalies from COSMIC-2 GNSS-RO from 2019 and 2024 as Potential Earthquake Precursors

by Badr-Eddine Boudriki Semlali, Carlos Molina, Hyuk Park and Adriano Camps

ISPRS Int. J. Geo-Inf. 2026, 15(3), 128; https://doi.org/10.3390/ijgi15030128 - 15 Mar 2026

Cited by 2 | Viewed by 935

Abstract

Currently, there are no consistent earthquake precursors for early warning. However, the correlation between earthquakes and ionospheric scintillation, measured using the S₄ index via GNSS-RO, is under active study. This research analyzes S₄ anomalies as a potential earthquake proxy, using GNSS-RO [...] Read more.

Currently, there are no consistent earthquake precursors for early warning. However, the correlation between earthquakes and ionospheric scintillation, measured using the S₄ index via GNSS-RO, is under active study. This research analyzes S₄ anomalies as a potential earthquake proxy, using GNSS-RO data from COSMIC-2/TGRS (Tri-GNSS Radio Occultation System) collected from 2019 to 2024. It examines over 71,000 global earthquakes within ±60° of the equator with magnitudes greater than 4. The quality of S₄ anomalies has been enhanced by filtering out space-weather-induced disturbances using the daily planetary geomagnetic index (Kp) and the solar activity flag collected from ground stations. The S₄ anomalies were calculated using robust statistical methods, such as the standard deviation and the interquartile range. This study evaluated the correlation with a confusion matrix, a receiver operating characteristic curve, and various figures of merit. The results demonstrated a promising positive S₄ anomaly between 1 and 7 days before the analyzed earthquakes, indicating the potential of ionospheric scintillation as an earthquake precursor, with the robust statistical methods employed instilling confidence in the validity of our findings. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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26 pages, 14594 KB  

Open AccessArticle

Mix-Persona Comment Generation and Geographically Enhanced Context Retrieval for LLM Fine-Tuning in Multimodal Crisis Post Classification

by Tong Bie, Yongli Hu, Yu Fu, Linjia Hao, Tengfei Liu, Kan Guo, Huajie Jiang, Junbin Gao, Yanfeng Sun and Baocai Yin

ISPRS Int. J. Geo-Inf. 2026, 15(3), 104; https://doi.org/10.3390/ijgi15030104 - 2 Mar 2026

Viewed by 1160

Abstract

Social media has become a vital source for humanitarian organizations to gather information during crises. However, existing multimodal classification methods operate primarily as isolated systems, while neglecting external references crucial for accurate judgment. Furthermore, while user comments can provide valuable context, they are [...] Read more.

Social media has become a vital source for humanitarian organizations to gather information during crises. However, existing multimodal classification methods operate primarily as isolated systems, while neglecting external references crucial for accurate judgment. Furthermore, while user comments can provide valuable context, they are often scarce during the early stages of a crisis. To address these limitations, we propose a framework named Mix-Persona Comment Generation with Geographically Enhanced Context Retrieval for LLM Instruction Fine-tuning (MPCG-GECR). To mitigate comment scarcity, we employ a Synthetic Persona Generator (SPG) that prompts LLMs to adopt diverse mix-personas, generating synthetic comments that simulate multi-perspective public discourse. To incorporate external references, we introduce a Geographically Enhanced Context Retrieval (GECR) module. Unlike standard retrieval approaches, GECR utilizes a hybrid re-ranking strategy to identify samples that are both multimodally similar and geographically consistent, serving as reliable reference anchors for the LLM. By integrating these social perspectives and geographic references into a unified instruction-tuning format, we transform the classification task into a context-aware text generation problem and fine-tune the LLM using Low-Rank Adaptation (LoRA). Extensive experiments on the CrisisMMD and DMD datasets demonstrate that MPCG-GECR effectively overcomes data scarcity and context isolation, significantly outperforming existing methods. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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34 pages, 14353 KB  

Open AccessArticle

Nationwide Prediction of Flood Damage Costs in the Contiguous United States Using ML-Based Models: A Data-Driven Approach

by Khaled M. Adel, Hany G. Radwan and Mohamed M. Morsy

Hydrology 2026, 13(1), 31; https://doi.org/10.3390/hydrology13010031 - 14 Jan 2026

Viewed by 808

Abstract

Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive robustness. This study develops a data-driven framework for [...] Read more.

Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive robustness. This study develops a data-driven framework for estimating flood damage costs across the contiguous United States, where comprehensive hydrologic, climatic, and socioeconomic data are available. A database of 17,407 flood events was compiled, incorporating approximately 38 parameters obtained from the National Oceanic and Atmospheric Administration (NOAA), the National Water Model (NWM), the United States Geological Survey (USGS NED), and the U.S. Census Bureau. Data preprocessing addressed missing values and outliers using the interquartile range and Walsh tests, followed by partitioning into training (70%), testing (15%), and validation (15%) subsets. Four modeling configurations were examined to improve predictive accuracy. The optimal hybrid regression–classification framework achieved correlation coefficients of 0.97 (training), 0.77 (testing), and 0.81 (validation) with minimal bias (−5.85, −107.8, and −274.5 USD, respectively). The findings demonstrate the potential of nationwide, event-based predictive approaches to enhance flood-damage cost assessment, providing a practical tool for risk evaluation and resource planning. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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18 pages, 30685 KB  

Open AccessArticle

Leveraging Explainable Artificial Intelligence for Place-Based and Quantitative Strategies in Urban Pluvial Flooding Management

by Chaorui Tan, Entong Ke and Haochen Shi

ISPRS Int. J. Geo-Inf. 2025, 14(12), 475; https://doi.org/10.3390/ijgi14120475 - 1 Dec 2025

Cited by 1 | Viewed by 866

Abstract

Reducing urban pluvial flooding susceptibility requires identifying dominant variables in different regions and offering quantitative management strategies, which remains a challenge for existing methodologies. To address this, this study delves into the characteristics of SHAP’s (Shapley Additive exPlanations) local interpretability and proposes a [...] Read more.

Reducing urban pluvial flooding susceptibility requires identifying dominant variables in different regions and offering quantitative management strategies, which remains a challenge for existing methodologies. To address this, this study delves into the characteristics of SHAP’s (Shapley Additive exPlanations) local interpretability and proposes a novel and concise framework based on explainable artificial intelligence (ensemble learning-SHAP) and applies it to the central urban area of Guangzhou as a case study. The research findings are as follows: (1) This framework captures the nonlinear and threshold effects of flood drivers, identifying specific inflection points where landscape features shift from mitigating to exacerbating flooding. (2) Anthropogenic variables, specifically impervious surface density (ISD) and vegetation (kNDVI), are identified as the dominant variables driving susceptibility in urban hotspots at the grid scale. (3) The interpretability results demonstrate high stability across model iterations. Finally, based on these findings, this study provides place-based and quantitative pluvial flooding management recommendations: for areas dominated by impervious surfaces and vegetation, maintaining the impervious surface density below 0.8 and the kNDVI above 0.25 can effectively reduce the susceptibility to urban flooding. This study provides a framework for achieving place-based and quantitative flooding management and offers valuable scientific insights for flooding management, urban planning, and sustainable urban development in the central district of Guangzhou, as well as in broader developing regions. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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17 pages, 6551 KB  

Open AccessArticle

Monitoring the Impacts of Human Activities on Groundwater Storage Changes Using an Integrated Approach of Remote Sensing and Google Earth Engine

by Sepide Aghaei Chaleshtori, Omid Ghaffari Aliabad, Ahmad Fallatah, Kamil Faisal, Masoud Shirali, Mousa Saei and Teodosio Lacava

Hydrology 2025, 12(7), 165; https://doi.org/10.3390/hydrology12070165 - 26 Jun 2025

Cited by 1 | Viewed by 3016

Abstract

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. [...] Read more.

Groundwater storage refers to the water stored in the pore spaces of underground aquifers, which has been increasingly affected by both climate change and anthropogenic activities in recent decades. Therefore, monitoring their changes and the factors that affect it is of great importance. Although the influence of natural factors on groundwater is well-recognized, the impact of human activities, despite being a major contributor to its change, has been less explored due to the challenges in measuring such effects. To address this gap, our study employed an integrated approach using remote sensing and the Google Earth Engine (GEE) cloud-free platform to analyze the effects of various anthropogenic factors such as built-up areas, cropland, and surface water on groundwater storage in the Lake Urmia Basin (LUB), Iran. Key anthropogenic variables and groundwater data were pre-processed and analyzed in GEE for the period from 2000 to 2022. The processes linking these variables to groundwater storage were considered. Built-up area expansion often increases groundwater extraction and reduces recharge due to impervious surfaces. Cropland growth raises irrigation demand, especially in semi-arid areas like the LUB, leading to higher groundwater use. In contrast, surface water bodies can supplement water supply or enhance recharge. The results were then exported to XLSTAT software2019, and statistical analysis was conducted using the Mann–Kendall (MK) non-parametric trend test on the variables to investigate their potential relationships with groundwater storage. In this study, groundwater storage refers to variations in groundwater storage anomalies, estimated using outputs from the Global Land Data Assimilation System (GLDAS) model. Specifically, these anomalies are derived as the residual component of the terrestrial water budget, after accounting for soil moisture, snow water equivalent, and canopy water storage. The results revealed a strong negative correlation between built-up areas and groundwater storage, with a correlation coefficient of −1.00. Similarly, a notable negative correlation was found between the cropland area and groundwater storage (correlation coefficient: −0.85). Conversely, surface water availability showed a strong positive correlation with groundwater storage, with a correlation coefficient of 0.87, highlighting the direct impact of surface water reduction on groundwater storage. Furthermore, our findings demonstrated a reduction of 168.21 mm (millimeters) in groundwater storage from 2003 to 2022. GLDAS represents storage components, including groundwater storage, in units of water depth (mm) over each grid cell, employing a unit-area, mass balance approach. Although storage is conceptually a volumetric quantity, expressing it as depth allows for spatial comparison and enables conversion to volume by multiplying by the corresponding surface area. Full article

(This article belongs to the Topic Natural Hazards Monitoring, Risk Assessment, Modelling and Management in the Artificial Intelligence Era)

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