Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2915-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2915-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
26 May 2023
Model description paper |
| 26 May 2023
| 26 May 2023
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
Guoding Chen
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
China Meteorological Administration Hydro-Meteorology Key Laboratory, Hohai University, Nanjing, Jiangsu, 210098, China
Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
Sheng Wang
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
Yi Xia
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
Lijun Chao
The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu, 210098, China
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, Jiangsu, 210098, China
College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, Jiangsu, 210098, China
Viewed
Total article views: 5,034 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Sep 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,761 | 1,149 | 124 | 5,034 | 121 | 185 |
- HTML: 3,761
- PDF: 1,149
- XML: 124
- Total: 5,034
- BibTeX: 121
- EndNote: 185
Total article views: 2,933 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 26 May 2023)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,219 | 632 | 82 | 2,933 | 102 | 167 |
- HTML: 2,219
- PDF: 632
- XML: 82
- Total: 2,933
- BibTeX: 102
- EndNote: 167
Total article views: 2,101 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 02 Sep 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,542 | 517 | 42 | 2,101 | 19 | 18 |
- HTML: 1,542
- PDF: 517
- XML: 42
- Total: 2,101
- BibTeX: 19
- EndNote: 18
Viewed (geographical distribution)
Total article views: 5,034 (including HTML, PDF, and XML)
Thereof 4,811 with geography defined
and 223 with unknown origin.
Total article views: 2,933 (including HTML, PDF, and XML)
Thereof 2,854 with geography defined
and 79 with unknown origin.
Total article views: 2,101 (including HTML, PDF, and XML)
Thereof 1,957 with geography defined
and 144 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
52 citations as recorded by crossref.
- Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques S. Shinde et al. https://doi.org/10.1186/s12302-024-00906-9
- Coupling hydrological, geotechnical and machine learning models to enhance landslide prediction for an early warning system: application to Upper Garonne River Basin, Pyrenees, Spain F. Asurza et al. https://doi.org/10.1007/s10346-025-02685-7
- Assessing access to safe drinking water in flood-affected areas of District Nowshera, Pakistan: A case study towards achieving sustainable development goal 6.1 M. Tufail et al. https://doi.org/10.1016/j.ecohyd.2024.07.001
- Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq K. Khafaji & B. Ben Hamed https://doi.org/10.7717/peerj-cs.2416
- Nutrition crisis management after floods: A multisectoral perspective A. Rakha et al. https://doi.org/10.1016/j.ijdrr.2024.105141
- Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models D. Nguyen et al. https://doi.org/10.32604/cmes.2024.056576
- Predicting landslide and debris flow susceptibility using Logitboost alternating decision trees and ensemble techniques C. Nguyen et al. https://doi.org/10.1007/s11069-024-06844-2
- Integrated debris flow hazard and risk assessment using UAV data and RAMMS, a case study in northern Pakistan N. Ahmad et al. https://doi.org/10.1007/s11069-024-06862-0
- Effect of topographic factors on the spatial distribution of surface soil moisture: A case study in the Shandian River Basin, China Y. Chu et al. https://doi.org/10.1016/j.ejrh.2026.103609
- Quantitative Model Construction for Sustainable Security Patterns in Social–Ecological Links Using Remote Sensing and Machine Learning L. Liu et al. https://doi.org/10.3390/rs15153837
- Research Fronts 2025: Mapping the evolving landscape of global science and technology H. Wang et al. https://doi.org/10.1016/j.xinn.2026.101427
- Exploring deep learning models for roadside landslide prediction: Insights and implications from comparative analysis T. Nguyen Viet et al. https://doi.org/10.1016/j.pce.2024.103741
- Characterization and Geomorphic Change Detection of Landslides Using UAV Multi-Temporal Imagery in the Himalayas, Pakistan N. Ahmad et al. https://doi.org/10.3390/land13070904
- Numerical Analysis of 3D Slope Stability in a Rainfall-Induced Landslide: Insights from Different Hydrological Conditions and Soil Layering G. Chen et al. https://doi.org/10.3390/w17223316
- Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO2 Concentrations: A Google Earth Engine-Driven Investigation X. Zheng et al. https://doi.org/10.1109/JSTARS.2024.3397496
- Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment S. Fu et al. https://doi.org/10.1016/j.pce.2024.103682
- Editorial: Emerging talents in water science: water and critical zone 2021/22 P. Luo et al. https://doi.org/10.3389/frwa.2024.1374081
- Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China F. Shi et al. https://doi.org/10.3390/s24134366
- PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale G. Chen et al. https://doi.org/10.1016/j.envsoft.2023.105882
- Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations A. Patel et al. https://doi.org/10.2166/wcc.2024.052
- Integration of the Non-linear Time Series GARCH Model with Fuzzy Model Optimized with Water Cycle Algorithm for River Streamflow Forecasting M. Karami et al. https://doi.org/10.1007/s44196-024-00570-0
- Rainfall-Induced Slope Stability Modeling: A Systematic Review of Coupled and Hybrid Frameworks A. Viswanath et al. https://doi.org/10.1007/s10706-026-03803-5
- A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting V. Kumar et al. https://doi.org/10.1007/s11269-024-03969-8
- Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection B. Zerouali et al. https://doi.org/10.1038/s41598-024-72588-5
- Measuring the effect of hydrological insecurity on landscape and ecological condition of floodplain wetland S. Pal et al. https://doi.org/10.1007/s10668-024-05248-3
- Assessing the probabilistic evolution of cascading hazards of landslide-river blockage-dam breaching-flood by integrating physics-based and data-driven methods M. Peng et al. https://doi.org/10.1016/j.ress.2025.112097
- Landslide susceptibility assessment using novel hybridized methods based on the support vector regression A. Jaafari https://doi.org/10.1016/j.ecoleng.2024.107372
- Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions H. Cui et al. https://doi.org/10.1016/j.compgeo.2024.106400
- Empowering a coupled hydrological-geotechnical model to simulate long-term vegetation dynamics and their impact on catchment-scale flood and landslide hazards G. Chen et al. https://doi.org/10.1016/j.jhydrol.2025.133225
- Integrating HEC-RAS, GIS, and LISREL for assessing and enhancing urban building resilience against flood threats: Comprehensive model and analysis F. Estelaji et al. https://doi.org/10.1016/j.heliyon.2024.e39463
- An ensemble approach to downscale climatological variables via screening of clustered ANNs Z. Razzaghzadeh et al. https://doi.org/10.2166/wcc.2024.255
- Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition S. Ladouali et al. https://doi.org/10.1016/j.ejrh.2024.101861
- Insights into Afikpo Synclinorium structures: Subsurface analysis and intrusion outlining from airborne magnetic data C. Akpa et al. https://doi.org/10.1016/j.jafrearsci.2024.105374
- Predicting Land Use Land Cover Dynamics and Land Surface Temperature Changes Using CA-Markov-Chain Models in Islamabad, Pakistan (1992–2042) M. Farhan et al. https://doi.org/10.1109/JSTARS.2024.3441241
- Optimization study on the selection method for non-landslide points based on landslide susceptibility zoning L. Liu et al. https://doi.org/10.1080/19475705.2025.2555728
- Review of flood monitoring and prevention approaches: a data analytic perspective S. Bukhari et al. https://doi.org/10.1007/s11069-024-07050-w
- A prototype adaptive mesh generator for enhancing computational efficiency and accuracy in physically-based modeling of flood-landslide hazards G. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106458
- A novel quantity assessment of landscape ecological risk using human-nature driving mechanism for sustainable society L. Liu et al. https://doi.org/10.1016/j.scitotenv.2024.173892
- Physically based model for assessing rainfall-induced deep-seated landslides using a hydrological-geotechnical model S. Phakdimek et al. https://doi.org/10.1080/19475683.2025.2481063
- The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning L. Liu et al. https://doi.org/10.3390/rs15184437
- Integrated air-space-ground-subsurface framework for comprehensive analysis of mountainous landslide behavior in Zanbatang T. Wang et al. https://doi.org/10.1016/j.asr.2025.12.024
- Assessing the vulnerability of selected coastal informal settlements to floods in the Old Brahmaputra River floodplain, Bangladesh M. Islam et al. https://doi.org/10.1016/j.uclim.2024.102078
- Seismically-induced permanent displacements of slopes using 3D Nested Newmark method Q. Li et al. https://doi.org/10.3389/feart.2024.1355767
- Evaluating socio-hydrological stress through potential stormwater harvesting sites using multi-criteria analysis in Mumbai, India N. Yadav et al. https://doi.org/10.1016/j.pce.2024.103715
- Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data M. Safarov et al. https://doi.org/10.1007/s11629-023-8538-z
- Landslide susceptibility assessment and mapping using new ensemble model Z. Shen et al. https://doi.org/10.1016/j.asr.2024.06.018
- Evaluation of Land Use Land Cover Changes in Response to Land Surface Temperature With Satellite Indices and Remote Sensing Data Q. Zhao et al. https://doi.org/10.1016/j.rama.2024.07.003
- Predictive Analysis of Slope Stability via Metaheuristic Algorithms Helping Neural Networks Y. Su & R. Li https://doi.org/10.1002/gj.5184
- Enhancing Geohazard management: real-time dynamic ascending dimension modeling for landslide risk assessment W. Hu et al. https://doi.org/10.1007/s10064-025-04365-1
- Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood S. Soomro et al. https://doi.org/10.1007/s11269-024-03894-w
- Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models N. Liu et al. https://doi.org/10.1007/s12145-024-01392-6
- Cloud computing-based estimation of Peninsular India’s long-term climate change impacts on rainfall, surface temperature, and geospatial indices B. Halder et al. https://doi.org/10.1080/19475705.2024.2381635
52 citations as recorded by crossref.
- Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques S. Shinde et al. https://doi.org/10.1186/s12302-024-00906-9
- Coupling hydrological, geotechnical and machine learning models to enhance landslide prediction for an early warning system: application to Upper Garonne River Basin, Pyrenees, Spain F. Asurza et al. https://doi.org/10.1007/s10346-025-02685-7
- Assessing access to safe drinking water in flood-affected areas of District Nowshera, Pakistan: A case study towards achieving sustainable development goal 6.1 M. Tufail et al. https://doi.org/10.1016/j.ecohyd.2024.07.001
- Employing deep learning in crisis management and decision making through prediction using time series data in Mosul Dam Northern Iraq K. Khafaji & B. Ben Hamed https://doi.org/10.7717/peerj-cs.2416
- Nutrition crisis management after floods: A multisectoral perspective A. Rakha et al. https://doi.org/10.1016/j.ijdrr.2024.105141
- Landslide Susceptibility Mapping Using RBFN-Based Ensemble Machine Learning Models D. Nguyen et al. https://doi.org/10.32604/cmes.2024.056576
- Predicting landslide and debris flow susceptibility using Logitboost alternating decision trees and ensemble techniques C. Nguyen et al. https://doi.org/10.1007/s11069-024-06844-2
- Integrated debris flow hazard and risk assessment using UAV data and RAMMS, a case study in northern Pakistan N. Ahmad et al. https://doi.org/10.1007/s11069-024-06862-0
- Effect of topographic factors on the spatial distribution of surface soil moisture: A case study in the Shandian River Basin, China Y. Chu et al. https://doi.org/10.1016/j.ejrh.2026.103609
- Quantitative Model Construction for Sustainable Security Patterns in Social–Ecological Links Using Remote Sensing and Machine Learning L. Liu et al. https://doi.org/10.3390/rs15153837
- Research Fronts 2025: Mapping the evolving landscape of global science and technology H. Wang et al. https://doi.org/10.1016/j.xinn.2026.101427
- Exploring deep learning models for roadside landslide prediction: Insights and implications from comparative analysis T. Nguyen Viet et al. https://doi.org/10.1016/j.pce.2024.103741
- Characterization and Geomorphic Change Detection of Landslides Using UAV Multi-Temporal Imagery in the Himalayas, Pakistan N. Ahmad et al. https://doi.org/10.3390/land13070904
- Numerical Analysis of 3D Slope Stability in a Rainfall-Induced Landslide: Insights from Different Hydrological Conditions and Soil Layering G. Chen et al. https://doi.org/10.3390/w17223316
- Coupling Remote Sensing Insights With Vegetation Dynamics and to Analyze NO2 Concentrations: A Google Earth Engine-Driven Investigation X. Zheng et al. https://doi.org/10.1109/JSTARS.2024.3397496
- Prediction of surface urban heat island based on predicted consequences of urban sprawl using deep learning: A way forward for a sustainable environment S. Fu et al. https://doi.org/10.1016/j.pce.2024.103682
- Editorial: Emerging talents in water science: water and critical zone 2021/22 P. Luo et al. https://doi.org/10.3389/frwa.2024.1374081
- Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China F. Shi et al. https://doi.org/10.3390/s24134366
- PHyL v1.0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale G. Chen et al. https://doi.org/10.1016/j.envsoft.2023.105882
- Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations A. Patel et al. https://doi.org/10.2166/wcc.2024.052
- Integration of the Non-linear Time Series GARCH Model with Fuzzy Model Optimized with Water Cycle Algorithm for River Streamflow Forecasting M. Karami et al. https://doi.org/10.1007/s44196-024-00570-0
- Rainfall-Induced Slope Stability Modeling: A Systematic Review of Coupled and Hybrid Frameworks A. Viswanath et al. https://doi.org/10.1007/s10706-026-03803-5
- A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting V. Kumar et al. https://doi.org/10.1007/s11269-024-03969-8
- Enhancing deep learning-based slope stability classification using a novel metaheuristic optimization algorithm for feature selection B. Zerouali et al. https://doi.org/10.1038/s41598-024-72588-5
- Measuring the effect of hydrological insecurity on landscape and ecological condition of floodplain wetland S. Pal et al. https://doi.org/10.1007/s10668-024-05248-3
- Assessing the probabilistic evolution of cascading hazards of landslide-river blockage-dam breaching-flood by integrating physics-based and data-driven methods M. Peng et al. https://doi.org/10.1016/j.ress.2025.112097
- Landslide susceptibility assessment using novel hybridized methods based on the support vector regression A. Jaafari https://doi.org/10.1016/j.ecoleng.2024.107372
- Fast physically-based probabilistic modelling of rainfall-induced shallow landslide susceptibility at the regional scale considering geotechnical uncertainties and different hydrological conditions H. Cui et al. https://doi.org/10.1016/j.compgeo.2024.106400
- Empowering a coupled hydrological-geotechnical model to simulate long-term vegetation dynamics and their impact on catchment-scale flood and landslide hazards G. Chen et al. https://doi.org/10.1016/j.jhydrol.2025.133225
- Integrating HEC-RAS, GIS, and LISREL for assessing and enhancing urban building resilience against flood threats: Comprehensive model and analysis F. Estelaji et al. https://doi.org/10.1016/j.heliyon.2024.e39463
- An ensemble approach to downscale climatological variables via screening of clustered ANNs Z. Razzaghzadeh et al. https://doi.org/10.2166/wcc.2024.255
- Short lead time standard precipitation index forecasting: Extreme learning machine and variational mode decomposition S. Ladouali et al. https://doi.org/10.1016/j.ejrh.2024.101861
- Insights into Afikpo Synclinorium structures: Subsurface analysis and intrusion outlining from airborne magnetic data C. Akpa et al. https://doi.org/10.1016/j.jafrearsci.2024.105374
- Predicting Land Use Land Cover Dynamics and Land Surface Temperature Changes Using CA-Markov-Chain Models in Islamabad, Pakistan (1992–2042) M. Farhan et al. https://doi.org/10.1109/JSTARS.2024.3441241
- Optimization study on the selection method for non-landslide points based on landslide susceptibility zoning L. Liu et al. https://doi.org/10.1080/19475705.2025.2555728
- Review of flood monitoring and prevention approaches: a data analytic perspective S. Bukhari et al. https://doi.org/10.1007/s11069-024-07050-w
- A prototype adaptive mesh generator for enhancing computational efficiency and accuracy in physically-based modeling of flood-landslide hazards G. Chen et al. https://doi.org/10.1016/j.envsoft.2025.106458
- A novel quantity assessment of landscape ecological risk using human-nature driving mechanism for sustainable society L. Liu et al. https://doi.org/10.1016/j.scitotenv.2024.173892
- Physically based model for assessing rainfall-induced deep-seated landslides using a hydrological-geotechnical model S. Phakdimek et al. https://doi.org/10.1080/19475683.2025.2481063
- The Influence of Visual Landscapes on Road Traffic Safety: An Assessment Using Remote Sensing and Deep Learning L. Liu et al. https://doi.org/10.3390/rs15184437
- Integrated air-space-ground-subsurface framework for comprehensive analysis of mountainous landslide behavior in Zanbatang T. Wang et al. https://doi.org/10.1016/j.asr.2025.12.024
- Assessing the vulnerability of selected coastal informal settlements to floods in the Old Brahmaputra River floodplain, Bangladesh M. Islam et al. https://doi.org/10.1016/j.uclim.2024.102078
- Seismically-induced permanent displacements of slopes using 3D Nested Newmark method Q. Li et al. https://doi.org/10.3389/feart.2024.1355767
- Evaluating socio-hydrological stress through potential stormwater harvesting sites using multi-criteria analysis in Mumbai, India N. Yadav et al. https://doi.org/10.1016/j.pce.2024.103715
- Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data M. Safarov et al. https://doi.org/10.1007/s11629-023-8538-z
- Landslide susceptibility assessment and mapping using new ensemble model Z. Shen et al. https://doi.org/10.1016/j.asr.2024.06.018
- Evaluation of Land Use Land Cover Changes in Response to Land Surface Temperature With Satellite Indices and Remote Sensing Data Q. Zhao et al. https://doi.org/10.1016/j.rama.2024.07.003
- Predictive Analysis of Slope Stability via Metaheuristic Algorithms Helping Neural Networks Y. Su & R. Li https://doi.org/10.1002/gj.5184
- Enhancing Geohazard management: real-time dynamic ascending dimension modeling for landslide risk assessment W. Hu et al. https://doi.org/10.1007/s10064-025-04365-1
- Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood S. Soomro et al. https://doi.org/10.1007/s11269-024-03894-w
- Estimation of static Young’s modulus of sandstone types: effective machine learning and statistical models N. Liu et al. https://doi.org/10.1007/s12145-024-01392-6
- Cloud computing-based estimation of Peninsular India’s long-term climate change impacts on rainfall, surface temperature, and geospatial indices B. Halder et al. https://doi.org/10.1080/19475705.2024.2381635
Saved (final revised paper)
Latest update: 17 Aug 2026
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a...
