48 citations as recorded by crossref.

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2. Snowmelt Flood Susceptibility Assessment in Kunlun Mountains Based on the Swin Transformer Deep Learning Method R. Yang et al. 10.3390/rs14246360
3. Regional-scale prediction of pluvial and flash flood susceptible areas using tree-based classifiers M. Kaiser et al. 10.1016/j.jhydrol.2022.128088
4. Physics-informed geostatistical approach (PIGA) for real-time fluvial flood modeling M. Kim et al. 10.1016/j.jhydrol.2024.131878
5. Flood susceptibility prediction using tree-based machine learning models in the GBA H. Lyu & Z. Yin 10.1016/j.scs.2023.104744
6. Flood Risk Assessment under Land Use and Climate Change in Wuhan City of the Yangtze River Basin, China Z. Li et al. 10.3390/land10080878
7. An AI/ML-Based Strategy for Disaster Response and Evacuation of Victims in Aged Care Facilities in the Hawkesbury-Nepean Valley: A Perspective H. Munawar et al. 10.3390/buildings12010080
8. Assessing multi-source random forest classification and robustness of predictor variables in flooded areas mapping C. Albertini et al. 10.1016/j.rsase.2024.101239
9. Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment A. Scorzini et al. 10.1038/s43247-024-01468-7
10. A Machine Learning Framework for Multi-Hazard Risk Assessment at the Regional Scale in Earthquake and Flood-Prone Areas A. Rocchi et al. 10.3390/app12020583
11. Predicting Flood Water Level Using Combined Hybrid Model of Rainfall-Runoff and AI-Based Models D. Kim et al. 10.1007/s12205-023-1147-0
12. AI and Disaster Risk: A Practitioner Perspective A. Moitra et al. 10.1145/3555163
13. Disaster resilience of optical networks: State of the art, challenges, and opportunities J. Rak et al. 10.1016/j.osn.2021.100619
14. Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model B. Pradhan et al. 10.1016/j.gsf.2023.101625
15. Machine Learning for Flood Prediction in Indonesia: Providing Online Access for Disaster Management Control R. Puspasari et al. 10.9719/EEG.2023.56.1.65
16. Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models J. Chen et al. 10.1016/j.jenvman.2021.112810
17. Nature-based solutions for flood risk reduction: A probabilistic modeling framework D. Lallemant et al. 10.1016/j.oneear.2021.08.010
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19. ANN-Based Assessment of Soft Surface Soil Layers’ Impact on Fault Rupture Propagation and Kinematic Distress of Gas Pipelines N. Makrakis et al. 10.3390/infrastructures8010006
20. Flood Hazard Assessment in Australian Tropical Cyclone-Prone Regions M. Kaspi & Y. Kuleshov 10.3390/cli11110229
21. A practical approach to flood hazard, vulnerability, and risk assessing and mapping for Quang Binh province, Vietnam H. Ha et al. 10.1007/s10668-021-02041-4
22. Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data G. Antzoulatos et al. 10.3390/su14063251
23. Towards an open pipeline for the detection of critical infrastructure from satellite imagery—a case study on electrical substations in The Netherlands J. De Plaen et al. 10.1088/2634-4505/ad63c9
24. Advancing flood damage modeling for coastal Alabama residential properties: A multivariable machine learning approach M. Museru et al. 10.1016/j.scitotenv.2023.167872
25. Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends C. Gonzales-Inca et al. 10.3390/w14142211
26. Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks M. Stojković et al. 10.2166/hydro.2023.069
27. Flood Damage Assessment: A Review of Microscale Methodologies for Residential Buildings O. Aribisala et al. 10.3390/su142113817
28. Spatio-temporal cross-validation to predict pluvial flood events in the Metropolitan City of Venice Z. Marco et al. 10.1016/j.jhydrol.2022.128150
29. A review on flood management technologies related to image processing and machine learning H. Munawar et al. 10.1016/j.autcon.2021.103916
30. An integrated Bayesian networks and Geographic information system (BNs-GIS) approach for flood disaster risk assessment: A case study of Yinchuan, China Y. Lu et al. 10.1016/j.ecolind.2024.112322
31. River-flood forecasting methods: the context of the Kelantan River in Malaysia M. Abdul Majid et al. 10.1088/1755-1315/880/1/012021
32. High-Precision AI-Enabled Flood Prediction Integrating Local Sensor Data and 3rd Party Weather Forecast Q. Wang & W. Abdelrahman 10.3390/s23063065
33. ForeINTiFlood: A Novel Framework for Forensic Investigation of Coastal Tidal Floods in The Pekalongan Coastal Area, Central Java, Indonesia F. Yulianto et al. 10.1007/s41748-024-00447-4
34. An Integrated Approach for Post-Disaster Flood Management Via the Use of Cutting-Edge Technologies and UAVs: A Review H. Munawar et al. 10.3390/su13147925
35. History, causes, and trend of floods in the U.S.: a review R. Abegaz et al. 10.1007/s11069-024-06791-y
36. Fairness and accountability of AI in disaster risk management: Opportunities and challenges C. Gevaert et al. 10.1016/j.patter.2021.100363
37. Single-family housing inventory projection method for natural hazard risk modeling applications C. Williams et al. 10.1007/s11069-023-06132-5
38. Coastal urban flood risk management: Challenges and opportunities − A systematic review F. Aziz et al. 10.1016/j.jhydrol.2024.132271
39. Enhancing urban flood forecasting in drainage systems using dynamic ensemble-based data mining F. Piadeh et al. 10.1016/j.watres.2023.120791
40. Pluvial flood risk assessment for 2021–2050 under climate change scenarios in the Metropolitan City of Venice E. Allegri et al. 10.1016/j.scitotenv.2024.169925
41. Enhancing Flood Management Through Machine Learning: A Comprehensive Analysis of the CatBoost Application O. O. I. et al. 10.38124/ijisrt/IJISRT24JUN1770
42. Pathways and challenges of the application of artificial intelligence to geohazards modelling A. Dikshit et al. 10.1016/j.gr.2020.08.007
43. Invited perspectives: Challenges and future directions in improving bridge flood resilience E. Tubaldi et al. 10.5194/nhess-22-795-2022
44. Flood hazards and susceptibility detection for Ganga river, Bihar state, India: Employment of remote sensing and statistical approaches Z. Yaseen 10.1016/j.rineng.2023.101665
45. A Spatial Improved-kNN-Based Flood Inundation Risk Framework for Urban Tourism under Two Rainfall Scenarios S. Liu et al. 10.3390/su13052859
46. Applying Machine Learning Techniques to Identify Key Factors Motivating Flood-Prone Residents to Implement Private Flood Mitigation Measures A. Fouladi Semnan et al. 10.1061/NHREFO.NHENG-1928
47. Application of GIS and Machine Learning to Predict Flood Areas in Nigeria E. Ighile et al. 10.3390/su14095039
48. Embedded Ethics for Responsible Artificial Intelligence Systems (EE-RAIS) in disaster management: a conceptual model and its deployment S. Afroogh et al. 10.1007/s43681-023-00309-1