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	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Hydrology hydrology	3.2	5.9	2014	15.7 Days	CHF 1800	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	7.2	2012	34.2 Days	CHF 1900	Submit
Remote Sensing remotesensing	4.1	8.6	2009	24.9 Days	CHF 2700	Submit
Smart Cities smartcities	5.5	14.7	2018	26.8 Days	CHF 2000	Submit

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17 pages, 6551 KiB  

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

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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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