{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:17:55Z","timestamp":1782181075210,"version":"3.54.5"},"reference-count":146,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2022A1515140066"],"award-info":[{"award-number":["2022A1515140066"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["222102220011"],"award-info":[{"award-number":["222102220011"]}]},{"name":"Henan Provincial key scientific and technological project","award":["2022A1515140066"],"award-info":[{"award-number":["2022A1515140066"]}]},{"name":"Henan Provincial key scientific and technological project","award":["222102220011"],"award-info":[{"award-number":["222102220011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.<\/jats:p>","DOI":"10.3390\/w16101407","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T06:14:42Z","timestamp":1715753682000},"page":"1407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Xinfeng","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Water Conservancy Engineering, Yellow River Conservancy Technical Institute, Kaifeng 475000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyan","family":"Wang","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyu","family":"Bai","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Xu","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengwen","family":"Dong","sequence":"additional","affiliation":[{"name":"Hubei Water Resources and Hydropower Science and Technology Promotion, Hubei Water Resources Research Institute, Wuhan 430070, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Rao","sequence":"additional","affiliation":[{"name":"Technical Research and Development Department, Wuhan Jianglai Measuring Equipment Co., Ltd., Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wuyi","family":"Ming","sequence":"additional","affiliation":[{"name":"Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002, China"},{"name":"Guangdong Provincial Key Laboratory of Digital Manufacturing Equipment, Guangdong HUST Industrial Technology Research Institute, Dongguan 523808, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41586-021-03695-w","article-title":"Satellite Imaging Reveals Increased Proportion of Population Exposed to Floods","volume":"596","author":"Tellman","year":"2021","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Oyelakin, R., Yang, W., and Krebs, P. (2024). Analysing Urban Flooding Risk with CMIP5 and CMIP6 Climate Projections. Water, 16.","DOI":"10.3390\/w16030474"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, H., Huang, J., Kang, J., and Han, D. (2018). Analysis of the Public Flood Risk Perception in a Flood-Prone City: The Case of Jingdezhen City in China. Water, 10.","DOI":"10.3390\/w10111577"},{"key":"ref_4","unstructured":"(2024, March 24). Available online: https:\/\/www.huxiu.com\/article\/446118.html."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1038\/d41586-021-01974-0","article-title":"The Fraction of the Global Population at Risk of Floods Is Growing","volume":"596","author":"Jongman","year":"2021","journal-title":"Nature"},{"key":"ref_6","unstructured":"(2024, March 28). Global Flood Database. Available online: https:\/\/global-flood-database.cloudtostreet.ai.\/."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"120057","DOI":"10.1016\/j.watres.2023.120057","article-title":"Deep Learning Enables Super-Resolution Hydrodynamic Flooding Process Modeling under Spatiotemporally Varying Rainstorms","volume":"239","author":"He","year":"2023","journal-title":"Water Res."},{"key":"ref_8","unstructured":"(2024, March 24). Libya|History, People, Map, & Government|Britannica. Available online: https:\/\/www.britannica.com\/event\/Libya-flooding-of-2023."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"De La Fuente, A., Meruane, V., and Meruane, C. (2019). Hydrological Early Warning System Based on a Deep Learning Runoff Model Coupled with a Meteorological Forecast. Water, 11.","DOI":"10.3390\/w11091808"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.jhydrol.2016.02.059","article-title":"Flood Forecasting and Alert System for Arda River Basin","volume":"541","author":"Artinyan","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_11","unstructured":"Li, Z. (2023). Deep Learning-Based Hydrological Time Series Prediction Model and Interpretability Quantitative Analysis Study. [Ph.D. Thesis, Huazhong University of Science and Technology]."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Li, Z., Kang, L., Zhou, L., and Zhu, M. (2021). Deep Learning Framework with Time Series Analysis Methods for Runoff Prediction. Water, 13.","DOI":"10.3390\/w13040575"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8558","DOI":"10.1029\/2018WR022643","article-title":"A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists","volume":"54","author":"Shen","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the Game of Go with Deep Neural Networks and Tree Search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly Accurate Protein Structure Prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100317","DOI":"10.1016\/j.egyai.2023.100317","article-title":"AI-Enabled Materials Discovery for Advanced Ceramic Electrochemical Cells","volume":"15","author":"Bello","year":"2024","journal-title":"Energy AI"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Choi, J.B., Nguyen, P.C.H., Sen, O., Udaykumar, H.S., and Baek, S. (2023). Artificial Intelligence Approaches for Energetic Materials by Design: State of the Art, Challenges, and Future Directions. Propellants Explos. Pyrotech., 48.","DOI":"10.1002\/prep.202200276"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114193","DOI":"10.1016\/j.rser.2023.114193","article-title":"Progress in Prediction of Remaining Useful Life of Hydrogen Fuel Cells Based on Deep Learning","volume":"192","author":"He","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5197","DOI":"10.1016\/j.ijhydene.2022.10.261","article-title":"A Systematic Review of Machine Learning Methods Applied to Fuel Cells in Performance Evaluation, Durability Prediction, and Application Monitoring","volume":"48","author":"Ming","year":"2023","journal-title":"Int. J. Hydrog. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"107868","DOI":"10.1016\/j.est.2023.107868","article-title":"Research Progress and Application of Deep Learning in Remaining Useful Life, State of Health and Battery Thermal Management of Lithium Batteries","volume":"70","author":"He","year":"2023","journal-title":"J. Energy Storage"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1007\/s11517-024-03042-x","article-title":"Recent Advances in the Precision Control Strategy of Artificial Pancreas","volume":"62","author":"Ming","year":"2024","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1134\/S1054661816010065","article-title":"A Survey of Deep Learning Methods and Software Tools for Image Classification and Object Detection","volume":"26","author":"Druzhkov","year":"2016","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, W., Liu, T., Han, Y., Ming, W., Du, J., Liu, Y., Yang, Y., Wang, L., Jiang, Z., and Wang, Y. (2022). A Review: The Detection of Cancer Cells in Histopathology Based on Machine Vision. Comput. Biol. Med., 146.","DOI":"10.1016\/j.compbiomed.2022.105636"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.measurement.2019.04.087","article-title":"Defect Detection of LGP Based on Combined Classifier with Dynamic Weights","volume":"143","author":"Ming","year":"2019","journal-title":"Measurement"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135657","DOI":"10.1109\/ACCESS.2021.3116131","article-title":"Review: Application of Convolutional Neural Network in Defect Detection of 3C Products","volume":"9","author":"Ming","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107722","DOI":"10.1016\/j.measurement.2020.107722","article-title":"A Comprehensive Review of Defect Detection in 3C Glass Components","volume":"158","author":"Ming","year":"2020","journal-title":"Measurement"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3315","DOI":"10.1016\/j.egyr.2023.09.175","article-title":"Comparing Long Short-Term Memory (LSTM) and Bidirectional LSTM Deep Neural Networks for Power Consumption Prediction","volume":"10","author":"Meneses","year":"2023","journal-title":"Energy Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lee, S.H., Lee, T., Kim, S., and Park, S. (2019, January 10\u201313). Energy Consumption Prediction System Based on Deep Learning with Edge Computing. Proceedings of the 2019 IEEE 2nd International Conference on Electronics Technology (ICET), Chengdu, China.","DOI":"10.1109\/ELTECH.2019.8839589"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"023303","DOI":"10.1063\/5.0038001","article-title":"Estimation of Weibull Parameters for Wind Energy Analysis across the UK","volume":"13","author":"Shu","year":"2021","journal-title":"J. Renew. Sustain. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"10441","DOI":"10.1007\/s12652-022-03701-7","article-title":"A Novel Intelligent Deep Learning Predictive Model for Meteorological Drought Forecasting","volume":"14","author":"Yaseen","year":"2023","journal-title":"J. Ambient. Intell. Hum. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"10218","DOI":"10.1109\/TGRS.2019.2931944","article-title":"Applying Deep Learning to Hail Detection: A Case Study","volume":"57","author":"Pullman","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, X., and Long, Z. (2023). E-Commerce Enterprises Financial Risk Prediction Based on FA-PSO-LSTM Neural Network Deep Learning Model. Sustainability, 15.","DOI":"10.3390\/su15075882"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s11782-020-00082-6","article-title":"Deep Learning in Finance and Banking: A Literature Review and Classification","volume":"14","author":"Huang","year":"2020","journal-title":"Front. Bus. Res. China"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4345","DOI":"10.5194\/hess-26-4345-2022","article-title":"Deep Learning Methods for Flood Mapping: A Review of Existing Applications and Future Research Directions","volume":"26","author":"Bentivoglio","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5639","DOI":"10.5194\/hess-22-5639-2018","article-title":"HESS Opinions: Incubating Deep-Learning-Powered Hydrologic Science Advances as a Community","volume":"22","author":"Shen","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/TMECH.2017.2728371","article-title":"Fault Diagnosis for Rotating Machinery Using Multiple Sensors and Convolutional Neural Networks","volume":"23","author":"Xia","year":"2018","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"116851","DOI":"10.1016\/j.energy.2019.116851","article-title":"Energy Optimization and Prediction Modeling of Petrochemical Industries: An Improved Convolutional Neural Network Based on Cross-Feature","volume":"194","author":"Geng","year":"2020","journal-title":"Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100296","DOI":"10.1016\/j.bdr.2021.100296","article-title":"NGCU: A New RNN Model for Time-Series Data Prediction","volume":"27","author":"Wang","year":"2022","journal-title":"Big Data Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ouyang, P., Yin, S., and Wei, S. (2017, January 18\u201322). A Fast and Power Efficient Architecture to Parallelize LSTM Based RNN for Cognitive Intelligence Applications. Proceedings of the 2017 54th ACM\/EDAC\/IEEE Design Automation Conference (DAC), Austin TX USA.","DOI":"10.1145\/3061639.3062187"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1007\/s12145-022-00830-7","article-title":"Evaluation of Deep Machine Learning-Based Models of Soil Cumulative Infiltration","volume":"15","author":"Sepahvand","year":"2022","journal-title":"Earth Sci. Inf."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"764","DOI":"10.2166\/hydro.2021.156","article-title":"A Deep Learning Technique-Based Automatic Monitoring Method for Experimental Urban Road Inundation","volume":"23","author":"Han","year":"2021","journal-title":"J. Hydroinformatics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"118973","DOI":"10.1016\/j.watres.2022.118973","article-title":"The Role of Deep Learning in Urban Water Management: A Critical Review","volume":"223","author":"Fu","year":"2022","journal-title":"Water Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e2022EA002385","DOI":"10.1029\/2022EA002385","article-title":"An End-To-End Flood Stage Prediction System Using Deep Neural Networks","volume":"10","author":"Windheuser","year":"2023","journal-title":"Earth Space Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.2166\/wcc.2024.517","article-title":"Comparison of Machine Learning Models for Flood Forecasting in the Mahanadi River Basin, India","volume":"15","author":"Sharma","year":"2024","journal-title":"J. Water Clim. Change"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Li, P., Zhang, J., and Krebs, P. (2022). Prediction of Flow Based on a CNN-LSTM Combined Deep Learning Approach. Water, 14.","DOI":"10.3390\/w14060993"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"128463","DOI":"10.1016\/j.jhydrol.2022.128463","article-title":"Short-Term Rainfall Forecasting Using Machine Learning-Based Approaches of PSO-SVR, LSTM and CNN","volume":"614","author":"Aderyani","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jiang, L., Hu, Y., Xia, X., Liang, Q., Soltoggio, A., and Kabir, S.R. (2020). A Multi-Scale Map Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs. Water, 12.","DOI":"10.3390\/w12051369"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"69053","DOI":"10.1109\/ACCESS.2018.2880044","article-title":"Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network","volume":"6","author":"Haidar","year":"2018","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.jhydrol.2004.10.008","article-title":"Nonstationary Hydrological Time Series Forecasting Using Nonlinear Dynamic Methods","volume":"307","author":"Coulibaly","year":"2005","journal-title":"J. Hydrol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1109\/78.348134","article-title":"Nonlinear Adaptive Prediction of Nonstationary Signals","volume":"43","author":"Haykin","year":"1995","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s11269-009-9439-9","article-title":"Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in E\u011firdir Lake Level Forecasting","volume":"24","author":"Tongal","year":"2010","journal-title":"Water Resour. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"101086","DOI":"10.1016\/j.uclim.2022.101086","article-title":"Flood Forecasting in Urban Reservoir Using Hybrid Recurrent Neural Network","volume":"42","author":"Cai","year":"2022","journal-title":"Urban. Clim."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kim, B.-J., Lee, Y.-T., and Kim, B.-H. (2022). A Study on the Optimal Deep Learning Model for Dam Inflow Prediction. Water, 14.","DOI":"10.3390\/w14172766"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, W., Zang, H., and Xu, D. (2023). Is the LSTM Model Better than RNN for Flood Forecasting Tasks? A Case Study of HuaYuankou Station and LouDe Station in the Lower Yellow River Basin. Water, 15.","DOI":"10.3390\/w15223928"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108604","DOI":"10.1016\/j.agwat.2023.108604","article-title":"Development of an Enhanced Bidirectional Recurrent Neural Network Combined with Time-Varying Filter-Based Empirical Mode Decomposition to Forecast Weekly Reference Evapotranspiration","volume":"290","author":"Karbasi","year":"2023","journal-title":"Agric. Water Manag."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"370","DOI":"10.2166\/wcc.2024.320","article-title":"Predicting the Peak Flow and Assessing the Hydrologic Hazard of the Kessem Dam, Ethiopia Using Machine Learning and Risk Management Centre-Reservoir Frequency Analysis Software","volume":"15","author":"Ayele","year":"2024","journal-title":"J. Water Clim. Change"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"124875","DOI":"10.1016\/j.jhydrol.2020.124875","article-title":"Sequence-Based Statistical Downscaling and Its Application to Hydrologic Simulations Based on Machine Learning and Big Data","volume":"586","author":"Wang","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"126371","DOI":"10.1016\/j.jhydrol.2021.126371","article-title":"Fusing Stacked Autoencoder and Long Short-Term Memory for Regional Multistep-Ahead Flood Inundation Forecasts","volume":"598","author":"Kao","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"127544","DOI":"10.1016\/j.jhydrol.2022.127544","article-title":"An Effective Alternative for Predicting Coastal Floodplain Inundation by Considering Rainfall, Storm Surge, and Downstream Topographic Characteristics","volume":"607","author":"Huang","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Botunac, I., Bosna, J., and Mateti\u0107, M. (2024). Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach. Information, 15.","DOI":"10.3390\/info15030136"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2470171","DOI":"10.1155\/2018\/2470171","article-title":"Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting","volume":"2018","author":"Choi","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.procs.2018.01.106","article-title":"Rolling Element Bearing Remaining Useful Life Estimation Based on a Convolutional Long-Short-Term Memory Network","volume":"127","author":"Hinchi","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e2021WR030185","DOI":"10.1029\/2021WR030185","article-title":"Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments","volume":"58","author":"Jiang","year":"2022","journal-title":"Water Resour. Res."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Hu, C., Wu, Q., Li, H., Jian, S., Li, N., and Lou, Z. (2018). Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation. Water, 10.","DOI":"10.3390\/w10111543"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"11030","DOI":"10.1002\/2017GL075619","article-title":"Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network","volume":"44","author":"Fang","year":"2017","journal-title":"Geophys. Res. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Le, X.-H., Ho, H.V., Lee, G., and Jung, S. (2019). Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting. Water, 11.","DOI":"10.3390\/w11071387"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.5194\/hess-26-3377-2022","article-title":"Deep Learning Rainfall\u2013Runoff Predictions of Extreme Events","volume":"26","author":"Frame","year":"2022","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kang, J., Wang, H., Yuan, F., Wang, Z., Huang, J., and Qiu, T. (2020). Prediction of Precipitation Based on Recurrent Neural Networks in Jingdezhen, Jiangxi Province, China. Atmosphere, 11.","DOI":"10.3390\/atmos11030246"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1175\/JHM-D-19-0169.1","article-title":"Near-Real-Time Forecast of Satellite-Based Soil Moisture Using Long Short-Term Memory with an Adaptive Data Integration Kernel","volume":"21","author":"Fang","year":"2020","journal-title":"J. Hydrometeorol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"125019","DOI":"10.1016\/j.jhydrol.2020.125019","article-title":"A Surrogate Model for the Variable Infiltration Capacity Model Using Deep Learning Artificial Neural Network","volume":"588","author":"Gu","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5194\/hess-27-139-2023","article-title":"Continuous Streamflow Prediction in Ungauged Basins: Long Short-Term Memory Neural Networks Clearly Outperform Traditional Hydrological Models","volume":"27","author":"Arsenault","year":"2023","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_72","first-page":"1421","article-title":"Streamflow Simulation in Data-Scarce Basins Using Bayesian and Physics-Informed Machine Learning Models","volume":"22","author":"Lu","year":"2021","journal-title":"J. Hydrometeorol."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Koutsovili, E.-I., Tzoraki, O., Theodossiou, N., and Tsekouras, G.E. (2023). Early Flood Monitoring and Forecasting System Using a Hybrid Machine Learning-Based Approach. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12110464"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"129521","DOI":"10.1016\/j.jhydrol.2023.129521","article-title":"A Novel Multi-Step Ahead Forecasting Model for Flood Based on Time Residual LSTM","volume":"620","author":"Zou","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"127553","DOI":"10.1016\/j.jhydrol.2022.127553","article-title":"Research on Particle Swarm Optimization in LSTM Neural Networks for Rainfall-Runoff Simulation","volume":"608","author":"Xu","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Forghanparast, F., and Mohammadi, G. (2022). Using Deep Learning Algorithms for Intermittent Streamflow Prediction in the Headwaters of the Colorado River, Texas. Water, 14.","DOI":"10.3390\/w14192972"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Dai, Z., Zhang, M., Nedjah, N., Xu, D., and Ye, F. (2023). A Hydrological Data Prediction Model Based on LSTM with Attention Mechanism. Water, 15.","DOI":"10.3390\/w15040670"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e2019WR025326","DOI":"10.1029\/2019WR025326","article-title":"A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning","volume":"56","author":"Xiang","year":"2020","journal-title":"Water Resour. Res."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Gu, Z., Th\u00e9, J.V.G., Yang, S.X., and Gharabaghi, B. (2022). The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models. Water, 14.","DOI":"10.3390\/w14111794"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1016\/j.jhydrol.2019.05.087","article-title":"Rapid Spatio-Temporal Flood Prediction and Uncertainty Quantification Using a Deep Learning Method","volume":"575","author":"Hu","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"105536","DOI":"10.1016\/j.cageo.2024.105536","article-title":"Incorporating Spatial Autocorrelation into Deformable ConvLSTM for Hourly Precipitation Forecasting","volume":"184","author":"Xu","year":"2024","journal-title":"Comput. Geosci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"127764","DOI":"10.1016\/j.jhydrol.2022.127764","article-title":"Effective Improvement of Multi-Step-Ahead Flood Forecasting Accuracy through Encoder-Decoder with an Exogenous Input Structure","volume":"609","author":"Cui","year":"2022","journal-title":"J. Hydrol."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"124631","DOI":"10.1016\/j.jhydrol.2020.124631","article-title":"Exploring a Long Short-Term Memory Based Encoder-Decoder Framework for Multi-Step-Ahead Flood Forecasting","volume":"583","author":"Kao","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_84","first-page":"0701001","article-title":"Surface Temperature Prediction of East China Sea Based on Variational Mode Decomposition-Long-Short Term Memory-Broad Learning System Hybrid Model","volume":"60","author":"Han","year":"2023","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1109\/LGRS.2017.2780843","article-title":"A CFCC-LSTM Model for Sea Surface Temperature Prediction","volume":"15","author":"Yang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.5194\/hess-25-2045-2021","article-title":"Rainfall\u2013Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network","volume":"25","author":"Gauch","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1109\/LGRS.2019.2947170","article-title":"Prediction of 3-D Ocean Temperature by Multilayer Convolutional LSTM","volume":"17","author":"Zhang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Sharma, R.K., Kumar, S., Padmalal, D., and Roy, A. (2023). Streamflow Prediction Using Machine Learning Models in Selected Rivers of Southern India. Int. J. River Basin Manag., 1\u201327.","DOI":"10.1080\/15715124.2023.2196635"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"6005","DOI":"10.5194\/hess-22-6005-2018","article-title":"Rainfall\u2013Runoff Modelling Using Long Short-Term Memory (LSTM) Networks","volume":"22","author":"Kratzert","year":"2018","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"5171","DOI":"10.1007\/s11269-023-03600-2","article-title":"Simulation of Urban Flood Process Based on a Hybrid LSTM-SWMM Model","volume":"37","author":"Zhao","year":"2023","journal-title":"Water Resour. Manag."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"5517","DOI":"10.5194\/hess-25-5517-2021","article-title":"Benchmarking Data-Driven Rainfall\u2013Runoff Models in Great Britain: A Comparison of Long Short-Term Memory (LSTM)-Based Models with Four Lumped Conceptual Models","volume":"25","author":"Lees","year":"2021","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"00368504241236557","DOI":"10.1177\/00368504241236557","article-title":"A Bibliometric Literature Review of Stock Price Forecasting: From Statistical Model to Deep Learning Approach","volume":"107","author":"Vuong","year":"2024","journal-title":"Sci. Prog."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.measurement.2019.06.038","article-title":"Degradation Evaluation of Slewing Bearing Using HMM and Improved GRU","volume":"146","author":"Wang","year":"2019","journal-title":"Measurement"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"36322","DOI":"10.1109\/ACCESS.2019.2905015","article-title":"Recent Progress on Generative Adversarial Networks (GANs): A Survey","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"5424","DOI":"10.1109\/JSTARS.2020.3022781","article-title":"Structure Aware Generative Adversarial Networks for Hyperspectral Image Classification","volume":"13","author":"Arefi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s11269-022-03076-6","article-title":"Green Roof Hydrological Modelling With GRU and LSTM Networks","volume":"36","author":"Xie","year":"2022","journal-title":"Water Resour. Manag."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"065012","DOI":"10.1088\/1748-9326\/ac7247","article-title":"Variational Bayesian Dropout with a Gaussian Prior for Recurrent Neural Networks Application in Rainfall\u2013Runoff Modeling","volume":"17","author":"Samadi","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Cho, M., Kim, C., Jung, K., and Jung, H. (2022). Water Level Prediction Model Applying a Long Short-Term Memory (LSTM)\u2013Gated Recurrent Unit (GRU) Method for Flood Prediction. Water, 14.","DOI":"10.3390\/w14142221"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, Z., Van Griensven Th\u00e9, J., Yang, S.X., and Gharabaghi, B. (2023). Flood Forecasting Using Hybrid LSTM and GRU Models with Lag Time Preprocessing. Water, 15.","DOI":"10.3390\/w15223982"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Kilinc, H.C., and Yurtsever, A. (2022). Short-Term Streamflow Forecasting Using Hybrid Deep Learning Model Based on Grey Wolf Algorithm for Hydrological Time Series. Sustainability, 14.","DOI":"10.3390\/su14063352"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Chhetri, M., Kumar, S., Pratim Roy, P., and Kim, B.-G. (2020). Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan. Remote Sens., 12.","DOI":"10.3390\/rs12193174"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1072","DOI":"10.2166\/nh.2023.072","article-title":"Prediction of Hourly Inflow for Reservoirs at Mountain Catchments Using Residual Error Data and Multiple-Ahead Correction Technique","volume":"54","author":"Guo","year":"2023","journal-title":"Hydrol. Res."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TIE.2017.2733438","article-title":"Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks","volume":"65","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"117291","DOI":"10.1016\/j.apenergy.2021.117291","article-title":"Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Using a Novel Forecasting Method","volume":"299","author":"Gu","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_105","first-page":"2098","article-title":"Bidirectional Convolutional Recurrent Neural Network Architecture with Group-Wise Enhancement Mechanism for Text Sentiment Classification","volume":"34","author":"Onan","year":"2022","journal-title":"J. King Saud. Univ. Comput. Inf. Sci."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Stateczny, A., Narahari, S.C., Vurubindi, P., Guptha, N.S., and Srinivas, K. (2023). Underground Water Level Prediction in Remote Sensing Images Using Improved Hydro Index Value with Ensemble Classifier. Remote Sens., 15.","DOI":"10.3390\/rs15082015"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"e5713","DOI":"10.1002\/cpe.5713","article-title":"Hybrid Model of Generative Adversarial Network and Takagi-Sugeno for Multidimensional Incomplete Hydrological Big Data Prediction","volume":"33","author":"Li","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Hofmann, J., and Sch\u00fcttrumpf, H. (2021). floodGAN: Using Deep Adversarial Learning to Predict Pluvial Flooding in Real Time. Water, 13.","DOI":"10.3390\/w13162255"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"129276","DOI":"10.1016\/j.jhydrol.2023.129276","article-title":"Generalizing Rapid Flood Predictions to Unseen Urban Catchments with Conditional Generative Adversarial Networks","volume":"618","author":"Giacomoni","year":"2023","journal-title":"J. Hydrol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1002\/2017WR022148","article-title":"Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network","volume":"54","author":"Laloy","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"130458","DOI":"10.1016\/j.jhydrol.2023.130458","article-title":"Deep Learning in Hydrology and Water Resources Disciplines: Concepts, Methods, Applications, and Research Directions","volume":"628","author":"Tripathy","year":"2024","journal-title":"J. Hydrol."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Ren, J., Ren, B., Zhang, Q., and Zheng, X. (2019). A Novel Hybrid Extreme Learning Machine Approach Improved by K Nearest Neighbor Method and Fireworks Algorithm for Flood Forecasting in Medium and Small Watershed of Loess Region. Water, 11.","DOI":"10.3390\/w11091848"},{"key":"ref_113","unstructured":"Zhang, E. (2020). Investigating Front Variations of Greenland Glaciers Using Multi-Temporal Remote Sensing Images and Deep Learning. [Ph.D. Thesis, Hong Kong University of Science and Technology (Hong Kong)]."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"e12964","DOI":"10.1111\/jfr3.12964","article-title":"Real-Time Prediction and Ponding Process Early Warning Method at Urban Flood Points Based on Different Deep Learning Methods","volume":"17","author":"Zhou","year":"2024","journal-title":"J. Flood Risk Manag."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"124379","DOI":"10.1016\/j.jhydrol.2019.124379","article-title":"Verification of Novel Integrations of Swarm Intelligence Algorithms into Deep Learning Neural Network for Flood Susceptibility Mapping","volume":"581","author":"Bui","year":"2020","journal-title":"J. Hydrol."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"132455","DOI":"10.1016\/j.jclepro.2022.132455","article-title":"Multiscale Homogenized Predictive Modelling of Flooding Surface in Urban Cities Using Physics-Induced Deep AI with UPC","volume":"363","author":"Chew","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"78492","DOI":"10.1109\/ACCESS.2020.2990181","article-title":"Joint Spatial and Temporal Modeling for Hydrological Prediction","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/17538947.2023.2172224","article-title":"Drainage Pattern Recognition Method Considering Local Basin Shape Based on Graph Neural Network","volume":"16","author":"Wang","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Hasan, M., Van Essen, B.C., Awwal, A.A.S., and Asari, V.K. (2019). A State-of-the-Art Survey on Deep Learning Theory and Architectures. Electronics, 8.","DOI":"10.3390\/electronics8030292"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"128566","DOI":"10.1016\/j.jclepro.2021.128566","article-title":"Deep Learning Models for Solar Irradiance Forecasting: A Comprehensive Review","volume":"318","author":"Kumari","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"5929","DOI":"10.1007\/s10462-020-09838-1","article-title":"A Review on the Long Short-Term Memory Model","volume":"53","author":"Mosquera","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"10639","DOI":"10.1109\/JIOT.2019.2940368","article-title":"Recurrent Neural Networks for Accurate RSSI Indoor Localization","volume":"6","author":"Hoang","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.jpdc.2022.03.010","article-title":"An Edge Intelligence Empowered Flooding Process Prediction Using Internet of Things in Smart City","volume":"165","author":"Chen","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"3085","DOI":"10.1007\/s11042-022-13339-4","article-title":"An Efficient Two-State GRU Based on Feature Attention Mechanism for Sentiment Analysis","volume":"83","author":"Zulqarnain","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1002\/int.22412","article-title":"An Attention-Based Category-Aware GRU Model for the next POI Recommendation","volume":"36","author":"Liu","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1016\/j.ceramint.2022.10.349","article-title":"Progress in Non-Traditional Machining of Amorphous Alloys","volume":"49","author":"Ming","year":"2023","journal-title":"Ceram. Int."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jclepro.2018.12.231","article-title":"Comparative Study of Energy Efficiency and Environmental Impact in Magnetic Field Assisted and Conventional Electrical Discharge Machining","volume":"214","author":"Ming","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.aej.2023.07.075","article-title":"Optimization of Process Parameters and Performance for Machining Inconel 718 in Renewable Dielectrics","volume":"79","author":"Ming","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"139017","DOI":"10.1109\/ACCESS.2023.3339561","article-title":"A Comprehensive Review of Deep Learning-Based PCB Defect Detection","volume":"11","author":"Chen","year":"2023","journal-title":"IEEE Access"},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhao, Y., Hu, S., Wang, H., Zhang, Y., and Ming, W. (2023). Progress in Active Infrared Imaging for Defect Detection in the Renewable and Electronic Industries. Sensors, 23.","DOI":"10.3390\/s23218780"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"120196","DOI":"10.1016\/j.jclepro.2020.120196","article-title":"Analyzing Sustainable Performance on High-Precision Molding Process of 3D Ultra-Thin Glass for Smart Phone","volume":"255","author":"Zhang","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.ress.2017.10.019","article-title":"Software Reliability Prediction Using a Deep Learning Model Based on the RNN Encoder\u2013Decoder","volume":"170","author":"Wang","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Forootan, M.M., Larki, I., Zahedi, R., and Ahmadi, A. (2022). Machine Learning and Deep Learning in Energy Systems: A Review. Sustainability, 14.","DOI":"10.3390\/su14084832"},{"key":"ref_134","first-page":"1125","article-title":"Applications of Deep Learning for Handwritten Chinese Character Recognition:A Review","volume":"42","author":"Jin","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/TSUSC.2018.2793284","article-title":"Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security","volume":"5","author":"Jiang","year":"2020","journal-title":"IEEE Trans. Sustain. Comput."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/JBHI.2018.2808281","article-title":"Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data","volume":"23","author":"Lu","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_137","first-page":"1","article-title":"MesoGRU: Deep Learning Framework for Mesoscale Eddy Trajectory Prediction","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1007\/s11709-023-0942-5","article-title":"Dynamic Prediction of Moving Trajectory in Pipe Jacking: GRU-Based Deep Learning Framework","volume":"17","author":"Yang","year":"2023","journal-title":"Front. Struct. Civ. Eng."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"104022","DOI":"10.1088\/1748-9326\/aba927","article-title":"Machine Learning Assisted Hybrid Models Can Improve Streamflow Simulation in Diverse Catchments across the Conterminous US","volume":"15","author":"Konapala","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_140","unstructured":"Wang, Y.-H. (2023). Bridging the Gap Between the Physical-Conceptual Approach and Machine Learning for Modeling Hydrological Systems. [Doctoral Dissertation, The University of Arizona]."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1126\/science.abe5650","article-title":"Geometric Deep Learning of RNA Structure","volume":"373","author":"Townshend","year":"2021","journal-title":"Science"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1126\/science.aaw4741","article-title":"Hidden Fluid Mechanics: Learning Velocity and Pressure Fields from Flow Visualizations","volume":"367","author":"Raissi","year":"2020","journal-title":"Science"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Fang, T., Chen, Y., Tan, H., Cao, J., Liao, J., and Huang, L. (2019). Flood Risk Evaluation in the Middle Reaches of the Yangtze River Based on Eigenvector Spatial Filtering Poisson Regression. Water, 11.","DOI":"10.3390\/w11101969"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"415","DOI":"10.3103\/S1068373917060073","article-title":"Spatial Autocorrelation Analysis of Extreme Precipitation in Iran","volume":"42","author":"Darand","year":"2017","journal-title":"Russ. Meteorol. Hydrol."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Zhuang, Q., Liu, S., and Zhou, Z. (2020). Spatial Heterogeneity Analysis of Short-Duration Extreme Rainfall Events in Megacities in China. Water, 12.","DOI":"10.3390\/w12123364"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"5700","DOI":"10.1002\/wrcr.20431","article-title":"Toward Computationally Efficient Large-Scale Hydrologic Predictions with a Multiscale Regionalization Scheme","volume":"49","author":"Kumar","year":"2013","journal-title":"Water Resour. Res."}],"container-title":["Water"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-4441\/16\/10\/1407\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:42:44Z","timestamp":1760107364000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-4441\/16\/10\/1407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,15]]},"references-count":146,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["w16101407"],"URL":"https:\/\/doi.org\/10.3390\/w16101407","relation":{},"ISSN":["2073-4441"],"issn-type":[{"value":"2073-4441","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,15]]}}}