45 citations as recorded by crossref.

1. Machine Learning in Tropical Cyclone Forecast Modeling: A Review R. Chen et al. https://doi.org/10.3390/atmos11070676
2. Artificial neural network approaches for disaster management: A literature review S. Guha et al. https://doi.org/10.1016/j.ijdrr.2022.103276
3. Application of surrogate models in estimation of storm surge:A comparative assessment A. Al Kajbaf & M. Bensi https://doi.org/10.1016/j.asoc.2020.106184
4. On water level forecasting using artificial neural networks: the case of the Río de la Plata Estuary, Argentina J. Dato et al. https://doi.org/10.1007/s11069-024-06585-2
5. Enhancing the Predicting Accuracy of the Water Stage Using a Physical-Based Model and an Artificial Neural Network-Genetic Algorithm in a River System W. Liu & C. Chung https://doi.org/10.3390/w6061642
6. Advancing spatio-temporal storm surge prediction with hierarchical deep neural networks S. Saviz Naeini et al. https://doi.org/10.1007/s11069-025-07428-4
7. Using surrogate modeling to predict storm surge on evolving landscapes under climate change M. Gharehtoragh & D. Johnson https://doi.org/10.1038/s44304-024-00032-9
8. Socioeconomic vulnerability assessment of coastal villages and buildings along Andhra Pradesh East Coast of India R. Mahendra et al. https://doi.org/10.1007/s10668-023-03955-x
9. Multi-order analytical solving computation of rainstorm causal decomposition during typhoons using a designed key–lock quasi-Newton optimizing derivation C. Huang et al. https://doi.org/10.1016/j.heliyon.2023.e20478
10. Machine learning based accurate storm surge peak and timing forecast in Pearl River Estuary R. Xiong et al. https://doi.org/10.3389/fmars.2026.1837410
11. The impact of extra-tropical transitioning on storm surge and waves in catastrophe risk modelling: application to the Japanese coastline N. Bruneau et al. https://doi.org/10.1007/s11069-016-2596-2
12. A Review of Application of Machine Learning in Storm Surge Problems Y. Qin et al. https://doi.org/10.3390/jmse11091729
13. Prediction of hourly inflow for reservoirs at mountain catchments using residual error data and multiple-ahead correction technique W. Guo et al. https://doi.org/10.2166/nh.2023.072
14. Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan W. Guo et al. https://doi.org/10.3390/w13070920
15. A framework for flexible peak storm surge prediction B. Pachev et al. https://doi.org/10.1016/j.coastaleng.2023.104406
16. Modeling the interaction between tides and storm surges for the Taiwan coast W. Liu et al. https://doi.org/10.1007/s10652-015-9441-0
17. Effects of tide-surge interactions on storm surges along the coast of the Bohai Sea, Yellow Sea, and East China Sea J. Xu et al. https://doi.org/10.1007/s11430-015-5251-y
18. Long-Lead-Time Prediction of Storm Surge Using Artificial Neural Networks and Effective Typhoon Parameters: Revisit and Deeper Insight W. Chao et al. https://doi.org/10.3390/w12092394
19. Modeling wetland aboveground biomass in the Poyang Lake National Nature Reserve using machine learning algorithms and Landsat-8 imagery R. Wan et al. https://doi.org/10.1117/1.JRS.12.046029
20. A Hybrid Approach Using Hydrodynamic Modeling and Artificial Neural Networks for Extreme Storm Surge Prediction M. Tayel & H. Oumeraci https://doi.org/10.1142/S0578563415400045
21. Nearshore Wave Predictions Using Data Mining Techniques during Typhoons: A Case Study near Taiwan’s Northeastern Coast C. Wei https://doi.org/10.3390/en11010011
22. A time-dependent surrogate model for storm surge prediction based on an artificial neural network using high-fidelity synthetic hurricane modeling S. Kim et al. https://doi.org/10.1007/s11069-014-1508-6
23. Modeling Study on the Asymmetry of Positive and Negative Storm Surges along the Southeastern Coast of China D. Chu et al. https://doi.org/10.3390/jmse9050458
24. Accurate storm surge forecasting using the encoder–decoder long short term memory recurrent neural network L. Bai & H. Xu https://doi.org/10.1063/5.0081858
25. Assessment of storm surge inundation and potential hazard maps for the southern coast of Taiwan W. Chen & W. Liu https://doi.org/10.1007/s11069-016-2199-y
26. Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm K. Sun & J. Pan https://doi.org/10.1029/2023EA003243
27. Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction X. Zhang & S. Jiang https://doi.org/10.1177/14750902211069551
28. Surge-NF: Neural Fields inspired peak storm surge surrogate modeling with multi-task learning and positional encoding W. Jiang et al. https://doi.org/10.1016/j.coastaleng.2024.104573
29. Prediction of Solitary Wave Forces on Coastal Bridge Decks Using Artificial Neural Networks G. Xu et al. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001215
30. Using neural network to improve sea level prediction along the southeastern Brazilian coast B. Primo de Siqueira & A. Paiva https://doi.org/10.1016/j.ocemod.2021.101898
31. Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre C. Davila Hernandez et al. https://doi.org/10.3390/a16050232
32. Sandy beach erosion: impacts and adaptation strategies in Thailand H. Thepsiriamnuay & N. Pumijumnong https://doi.org/10.1007/s10668-024-04763-7
33. Storm surge forecasting using TCWiSE synthetic typhoons with an LSTM model C. Chen et al. https://doi.org/10.1016/j.oceaneng.2026.126723
34. Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm H. Ouyang https://doi.org/10.5194/nhess-16-1897-2016
35. Investigating a complex lake-catchment-river system using artificial neural networks: Poyang Lake (China) Y. Li et al. https://doi.org/10.2166/nh.2015.150
36. Numerical study of the storm surges caused by increasing wind speeds in the Bohai Sea, Yellow Sea and East China Sea J. Xu et al. https://doi.org/10.1088/1755-1315/170/3/032127
37. Error-correction-based data-driven models for multiple-hour-ahead river stage predictions: A case study of the upstream region of the Cho-Shui River, Taiwan W. Guo et al. https://doi.org/10.1016/j.ejrh.2023.101378
38. A novel hybrid machine learning model for rapid assessment of wave and storm surge responses over an extended coastal region S. Saviz Naeini & R. Snaiki https://doi.org/10.1016/j.coastaleng.2024.104503
39. Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan S. Chiang et al. https://doi.org/10.3390/w14020191
40. Modeling Flood Inundation Induced by River Flow and Storm Surges over a River Basin W. Chen & W. Liu https://doi.org/10.3390/w6103182
41. Relative accuracy of HWRF reanalysis and a parametric wind model during the landfall of Hurricane Florence and the impacts on storm surge simulations M. Rahman et al. https://doi.org/10.1007/s11069-022-05702-3
42. Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model W. Chao & C. Young https://doi.org/10.3390/w14010096
43. Advancing storm surge forecasting from scarce observation data: A causal-inference based Spatio-Temporal Graph Neural Network approach W. Jiang et al. https://doi.org/10.1016/j.coastaleng.2024.104512
44. A Hybrid Novel Deep Learning Approach Applied for Time-Series Typhoon-Induced Storm Surge Prediction: A Case Study on Taiwan Northeastern Coast W. Chao & T. Kuo https://doi.org/10.1109/ACCESS.2026.3652640
45. Coastal inundation due to tropical cyclones along the east coast of India: an influence of climate change impact A. Rao et al. https://doi.org/10.1007/s11069-020-03861-9