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Water, Geohazards, and Artificial Intelligence, 2nd Edition
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Water, Geohazards, and Artificial Intelligence, 2nd Edition

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 7228

Special Issue Editor

Dr. Reza Derakhshani

E-Mail Website
Guest Editor
Department of Earth Sciences, Utrecht University, Utrecht, Netherlands
Interests: environmental hydrogeology; artificial intelligence; geohazards; morphotectonics; geology; Earth sciences
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing global impact of geohazards, which is connected to ongoing climate change, weathering, hydrological changes, subsidence, a lack of vegetation, and human activities, emphasizes the role of water from different viewpoints and at different scales; these range from microscopic porous media to regional studies and from modeling based on observed data to laboratory experiments, which appear to be the most promising approaches for studying water-related geohazards.

On this basis, this Special Issue focuses on recent advances in water-related geohazards using artificial intelligence and integrated methods.

We would like to invite scientists in this field to contribute to this Special Issue, which will focus broadly on the review, analysis, mapping, prediction, experimentation, susceptibility analysis, monitoring, and modeling of water-related geohazards such as landslides and slope instabilities, as well as the analysis of early-warning definitions based on artificial intelligence findings.

We welcome contributions on newly developed monitoring instruments, methods, techniques, and approaches, as well as relevant case studies on water, geohazards, and AI. Topics of interest include landslides, qanats, slope stabilities, floods, geotechnical hazard mapping, porous media, and their cascading combinations.

Dr. Reza Derakhshani
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • landslides
  • slope stability
  • artificial intelligence
  • experiments and models
  • natural hazards
  • porous media
  • geotechnical hazards
  • geomorphology and climate change
  • remote sensing and GIS analysis
  • morphotectonics
  • water basins
  • watershed morphometric indices
  • debris flow
  • water table
  • groundwater seepage
  • failure mechanism
  • hydrogeology

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Published Papers (4 papers)

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Research

19 pages, 11753 KiB  
Article
Landslide Deformation Analysis and Prediction with a VMD-SA-LSTM Combined Model
by Chengzhi Wen, Hongling Tian, Xiaoyan Zeng, Xin Xia, Xiaobo Hu and Bo Pang
Water 2024, 16(20), 2945; https://doi.org/10.3390/w16202945 - 16 Oct 2024
Cited by 2 | Viewed by 1297
Abstract
The evolution of landslides is influenced by the complex interplay of internal geological factors and external triggering factors, resulting in nonlinear dynamic changes. Although deep learning methods have demonstrated advantages in predicting multivariate landslide displacement, their performance is often constrained by the challenges [...] Read more.
The evolution of landslides is influenced by the complex interplay of internal geological factors and external triggering factors, resulting in nonlinear dynamic changes. Although deep learning methods have demonstrated advantages in predicting multivariate landslide displacement, their performance is often constrained by the challenges of extracting intricate features from extended time-series data. To address this challenge, we propose a novel displacement prediction model that integrates Variational Mode Decomposition (VMD), Self-Attention (SA), and Long Short-Term Memory (LSTM) networks. The model first employs VMD to decompose cumulative landslide displacement into trend, periodic, and stochastic components, followed by an assessment of the correlation between these components and the triggering factors using grey relational analysis. Subsequently, the self-attention mechanism is incorporated into the LSTM model to enhance its ability to capture complex dependencies. Finally, each displacement component is fed into the SA-LSTM model for separate predictions, which are then reconstructed to obtain the cumulative displacement prediction. Using the Zhonghai Village tunnel entrance (ZVTE) landslide as a case study, we validated the model with displacement data from GPS point 105 and made predictions for GPS point 104 to evaluate the model’s generalization capability. The results indicated that the RMSE and MAPE for SA-LSTM, LSTM, and TCN-LSTM at GPS point 105 were 0.3251 and 1.6785, 0.6248 and 2.9130, and 1.1777 and 5.5131, respectively. These findings demonstrate that SA-LSTM outperformed the other models in terms of complex feature extraction and accuracy. Furthermore, the RMSE and MAPE at GPS point 104 were 0.4232 and 1.0387, further corroborating the model’s strong extrapolation capability and its effectiveness in landslide monitoring. Full article
(This article belongs to the Special Issue Water, Geohazards, and Artificial Intelligence, 2nd Edition)
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28 pages, 26581 KiB  
Article
Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands
by Mojtaba Zaresefat, Reza Derakhshani and Jasper Griffioen
Water 2024, 16(18), 2581; https://doi.org/10.3390/w16182581 - 12 Sep 2024
Cited by 7 | Viewed by 2499
Abstract
No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging [...] Read more.
No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling. Full article
(This article belongs to the Special Issue Water, Geohazards, and Artificial Intelligence, 2nd Edition)
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31 pages, 8433 KiB  
Article
Groundwater Dynamics in African Endorheic Basins in Arid to Semi-Arid Transition Zones: The Batha Aquifer System, NE Chad
by Abakar Bourma Arrakhais, Abderamane Hamit, Claude Fontaine, Fatima Abdelfadel, Moustapha Dinar and Moumtaz Razack
Water 2024, 16(14), 2067; https://doi.org/10.3390/w16142067 - 22 Jul 2024
Viewed by 1738
Abstract
This study investigates the Batha endorheic basin in Chad, situated east of the Lake Chad basin in the arid to semi-arid Sahelian zone. This region has not yet undergone comprehensive geological and hydrogeological studies. More broadly, the transition zone between semi-arid and arid [...] Read more.
This study investigates the Batha endorheic basin in Chad, situated east of the Lake Chad basin in the arid to semi-arid Sahelian zone. This region has not yet undergone comprehensive geological and hydrogeological studies. More broadly, the transition zone between semi-arid and arid climates has been minimally explored. This research aims to evaluate the resources and dynamics of this multi-layered system using a combined geology-hydrogeology-hydrochemistry-isotopes approach. The multilayer system includes sedimentary layers (Quaternary, Pliocene, and Eocene) over a crystalline basement. A piezometric investigation of the system shows a general SE–NW groundwater, indicating an interconnection between all layers. Hydrochemical analyses identifies four main facies (calcium-bicarbonate, sodium-bicarbonate, sulphate-sodium, and mixed), primarily controlled by water–rock interaction with secondary influences from base-exchange and evaporation. Saturation indices indicate that these waters are close to equilibrium with the calcite-Mg phases, gaylussite and gypsum. Stable isotopes (oxygen-18 and deuterium) categorize groundwater into three groups: ancient water, recent and older meteoric water mixtures affected by evaporation, and mixtures more heavily impacted by evaporation. Tritium contents reveal three groups: current rainwater, modern water, and sub-modern water. These results indicate that ionic and isotopic differentiations cannot be strictly linked to specific layers, confirming the interconnected nature of the Batha system. The observed heterogeneity is mainly influenced by lithological and climatic variations. This study, though still limited, enhances significantly the understanding of the basin’s functioning and supports the rational exploitation of its vital resources for the Batha area’s development. Future investigations to complete the present study are highlighted. Full article
(This article belongs to the Special Issue Water, Geohazards, and Artificial Intelligence, 2nd Edition)
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14 pages, 20727 KiB  
Article
Radar Interferometry for Sustainable Groundwater Use: Detecting Subsidence and Sinkholes in Kabodarahang Plain
by Mohammad Mohammadhasani, Ahmad Rashidi, Behnaz Sheikh Shariati Kermani, Majid Nemati and Reza Derakhshani
Water 2024, 16(14), 1976; https://doi.org/10.3390/w16141976 - 12 Jul 2024
Viewed by 1149
Abstract
This study investigates the consequences of unsustainable groundwater extraction in the Kabodarahang plain, a region significantly impacted by geohazards, such as land subsidence and sinkhole formation due to excessive groundwater use for agricultural and industrial activities. Over 32 years (1990–2022), a dramatic decrease [...] Read more.
This study investigates the consequences of unsustainable groundwater extraction in the Kabodarahang plain, a region significantly impacted by geohazards, such as land subsidence and sinkhole formation due to excessive groundwater use for agricultural and industrial activities. Over 32 years (1990–2022), a dramatic decrease in groundwater levels by approximately ~41 m has been observed, leading to notable geohazards. Employing radar interferometry techniques with Sentinel-1 satellite radar imagery and the Sentinel Application Platform (SNAP) tool, complemented by field data, this research aims to quantify the rate of subsidence and evaluate the associated risks, particularly in urban and residential zones. Findings from 2017 to 2018 indicate a subsidence rate of 14.5 cm, predominantly in urban areas, thereby elevating the risk of this geohazard. The results underscore the critical need for sustainable groundwater management policies and practices. The study demonstrates the effectiveness of radar interferometry in monitoring subsidence in the Kabodarahang plain and suggests that integrating such techniques with field surveys and satellite data can enhance the detection and management of risks related to unsustainable groundwater usage. This research contributes to the understanding of the impacts of groundwater depletion on geohazards and supports the development of strategies for sustainable groundwater use to mitigate such risks. Full article
(This article belongs to the Special Issue Water, Geohazards, and Artificial Intelligence, 2nd Edition)
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