139 citations as recorded by crossref.

1. A Hybrid AE-SDGC-Autoformer Model for Short-Term Runoff Forecasting and Sustainable Water Resource Management R. Liu et al.
2. Water Resources’ AI–ML Data Uncertainty Risk and Mitigation Using Data Assimilation N. Martin & J. White
3. Generative Artificial Intelligence for Environmental Assessment: A New Paradigm for Sustainability Analysis S. Rahman et al.
4. Advances in artificial intelligence for climate science methods applications and evaluation challenges E. Houssein et al.
5. Forecasting monthly rainfall and temperature patterns in Van Province, Türkiye, using ARIMA and SARIMA models: a long-term climate analysis V. Yavuz
6. STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction Y. Chen et al.
7. In-depth simulation of rainfall–runoff relationships using machine learning methods M. Fuladipanah et al.
8. FROSTBYTE: a reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America L. Arnal et al.
9. Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al.
10. Modelos de Aprendizaje Automático Híbridos para Pronóstico Macroeconómico con Series Temporales de Alta Frecuencia G. Quirola Quizhpi & C. Inca Balseca
11. Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin Z. Mei et al.
12. Leveraging hybrid deep learning architectures for predicting monthly precipitation in Victoria, Australia E. Abdi et al.
13. A review of hybrid deep learning applications for streamflow forecasting K. Ng et al.
14. Forecasting bathing water quality in the UK: A critical review K. Krupska et al.
15. Forecasting Short-Term Export Volumes with Hybrid Models Integrating SARIMA with Attention-Based LSTM W. Wang et al.
16. A statistical–dynamical approach for probabilistic prediction of sub-seasonal precipitation anomalies over 17 hydroclimatic regions in China Y. Li et al.
17. Ensemble Streamflow Simulations in a Qinghai–Tibet Plateau Basin Using a Deep Learning Method with Remote Sensing Precipitation Data as Input J. Wang et al.
18. Future water stress in arid landscapes projected with GeoAI M. Rahman et al.
19. Advancing Subseasonal Extreme Rainfall Forecasting in Vietnam Using Machine Learning T. Cong et al.
20. Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets F. Hasan et al.
21. AI for atmosphere–ocean sciences: advancements, challenges and ways forward J. Luo et al.
22. Synthetic Forecast Ensembles for Evaluating Forecast Informed Reservoir Operations Z. Brodeur et al.
23. Artificial intelligence methods for rainy season forecasting: a comprehensive analysis B. Sonon et al.
24. Advancing Sub-Seasonal to Seasonal Streamflow Forecasting in Canada: A Review of Conventional and Emerging Approaches for Operational Applications D. Nguyen et al.
25. Machine Learning for Wind Pattern Estimation at Data-Scarce Coastal Ports: A Comparative Study Using Real Measurements A. Giannopoulos et al.
26. Internal structure modification of a simple monthly water balance model via incorporation of a machine learning-based nonlinear routing U. Okkan et al.
27. Evaluation of climate prediction models in Yunnan, China: traditional methods and AI approaches J. Zhao et al.
28. Groundwater depth prediction based on CNN-GRU-attention model H. Wei et al.
29. Hourly streamflow forecasting across diverse climate zones on Oʻahu Island, Hawaiʻi P. Parisouj et al.
30. Superior performance of hybrid model in ungauged basins for real-time hourly water level forecasting – A case study on the Lancang-Mekong mainstream Z. Dong et al.
31. Assessing the impact of hydro-climatic factors on surface water quality: A global review and synthesis A. Bamal et al.
32. Enhancing representation of data-scarce reservoir-regulated river basins using a hybrid DL-process based approach L. Deng et al.
33. Landscape metrics selection and influence over hydrological signatures: land use change’s effect over water resources in Chile L. Henriquez-Dole et al.
34. Random Forest-Based Multi-Objective Optimization Design Method of Relief Wells for Levee Safety A. An et al.
35. Ecosystem stability assessment under hydroclimatic anomalies in the arid region of Northwest China S. Chang et al.
36. Multi-source heterogeneous data-driven interpretable model based on transformer and kolmogorov-arnold networks for streamflow prediction C. Ding et al.
37. Hybrid random forest– artificial neural network model based forecasting of anthracnose in bottle gourd across different transplanting windows A. Chittaragi et al.
38. Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al.
39. Hydrological model parameter regionalization: Runoff estimation using machine learning techniques in the Tha Chin River Basin, Thailand P. Hlaing et al.
40. Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts T. Charoensuk et al.
41. Seasonal forecasts have sufficient skill to inform some agricultural decisions A. Kondal et al.
42. Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al.
43. Combining AI with urban resilience planning: A bibliometric analysis of literature S. Praharaj
44. Combining physical models and machine learning for enhanced soil moisture estimation M. Li et al.
45. Advancing aquifer recharge forecasting through hybrid explainable AI and hydrological modeling C. Sharma et al.
46. A hybrid deep learning framework for improving short-term precipitation forecasts S. Salehi & S. Akbar Salehi Neyshabouri
47. Machine learning generated streamflow drought forecasts for the conterminous United States (CONUS): developing and evaluating an operational tool to enhance sub-seasonal to seasonal streamflow drought early warning for gaged locations J. Hammond et al.
48. Tropical Cyclones Across Global Basins: Dynamics, Tracking Algorithms, Forecasting, and Emerging Scientometric Research Trends V. Singh et al.
49. An extension of WeatherBench 2 to binary hydroclimatic forecasts T. Zhao et al.
50. Human Versus Natural Influences on Climate and Biodiversity: The Carbon Dioxide Connection W. Davis
51. AI-Driven Ensemble Learning for Spatio-Temporal Rainfall Prediction in the Bengawan Solo River Watershed, Indonesia J. Jumadi et al.
52. Climate-induced changes in streamflow and nitrogen loading to Long Island Sound M. Duvall & J. Hagy
53. Enhanced rainfall nowcasting of tropical cyclone by an interpretable deep learning model and its application in real-time flood forecasting L. Liu et al.
54. A Hybrid Approach to Physical and Deep Learning Models for Radar-Based Precipitation Nowcasting H. Kim et al.
55. A Mamba-Based Hierarchical Partitioning Framework for Upper-Level Wind Field Reconstruction W. Chen et al.
56. Advances and challenges in multi‑scale water environment system modeling: from process simulation to a novel simulator architecture R. Xia et al.
57. Towards robust seasonal streamflow forecasts in mountainous catchments: impact of calibration metric selection in hydrological modeling D. Araya et al.
58. Holistic uncertainty quantification and attribution for real-time seasonal streamflow predictions: Insights from input, parameter and initial condition L. Liu et al.
59. Machine learning and deep learning in river-basin modeling: A comprehensive review M. Waqas & M. Nawaz
60. How suitable are copula models for post-processing global precipitation forecasts? Z. Huang & T. Zhao
61. Prediction of unsaturated zone soil moisture using an LSTM model driven by a physics-based model X. Lv et al.
62. Revolutionizing the future of hydrological science: Impact of machine learning and deep learning amidst emerging explainable AI and transfer learning R. Maity et al.
63. Predicting precipitation using dynamic distributed lag models in arid and sub-humid regions of South Africa L. Chaka et al.
64. Forecasting of wind farm power output based on dynamic loading of power transformer at the substation M. Hartmann et al.
65. Comparative analysis of machine learning models for water level forecasting in the Karang Mumus River, Indonesia F. Aditama et al.
66. Editorial: Data-driven machine learning for advancing hydrological and hydraulic predictability D. Lu et al.
67. Runoff simulation based on granular computing by introducing terrain factors to construct climate characteristic index Y. Zhao et al.
68. Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions H. Tao et al.
69. Prognosticators for precipitation variability adopting principal component regression analysis E. Aamir & A. Ghumman
70. Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand M. Wesselkamp et al.
71. Enhancing seasonal fire predictions with hybrid dynamical and random forest models M. Torres-Vázquez et al.
72. Artificial Intelligence in Hydrology: Advancements in Soil, Water Resource Management, and Sustainable Development S. Biazar et al.
73. Comparison between Two-Level Machine Learning and Deep Learning for Groundwater Potential Mapping in the Rmel Aquifer (Northwestern Morocco) M. Chahid et al.
74. Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm M. Al Mamun et al.
75. Assessing and optimizing high-resolution global river streamflow estimates with triple collocation analysis M. Sun et al.
76. Simulación del caudal en España utilizando redes neuronales Long Short-Term Memory J. Casado-Rodríguez et al.
77. Better continental-scale streamflow predictions for Australia: LSTM as a land surface model post-processor and standalone hydrological model A. Shokri et al.
78. A Review of the Advances and Emerging Approaches in Hydrological Forecasting: From Traditional to AI-Powered Models K. Robles et al.
79. Global hydrological reanalyses: The value of river discharge information for world‐wide downstream applications – The example of the Global Flood Awareness System GloFAS C. Prudhomme et al.
80. Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting C. Liu et al.
81. Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al.
82. A hybrid statistical-dynamical forecast of seasonal streamflow for a catchment in the Upper Columbia River basin in Canada T. Swift-LaPointe et al.
83. Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models L. Li et al.
84. Artificial intelligence in environmental remote sensing: Progress, way forward and key considerations C. Maniyar et al.
85. Short-Term Photovoltaic Power Forecasting Based on CNN–LSTM–Attention Mechanism M. Lei et al.
86. Hybridizing Machine and Deep Learning for Urban Water Demand Forecasting: An Ensemble Framework Leveraging Dam Monitoring Data M. Akiner
87. Interpretable Adaptive Multiscale Spatiotemporal Network for Long-Term Global Sea Surface Temperature Prediction R. Hao et al.
88. A comprehensive review of machine learning and deep learning approaches for rainfall forecasting: current progress, challenges, and future directions J. Senjaliya et al.
89. Hybrid modeling of first-principles and machine learning: A step-by-step tutorial review for practical implementation P. Shah et al.
90. Renewable microgrid optimization using AI: A B-SLR approach and future research directions G. Sarin et al.
91. Investigating Intelligent Forecasting and Optimization in Electrical Power Systems: A Comprehensive Review of Techniques and Applications S. Sharifhosseini et al.
92. Analyzing the Relationship between Agricultural AI Adoption and Government-Subsidized Insurance C. Osorio et al.
93. Fusion of data-driven models with a knowledge-guided loss function for flood forecasting H. Malik et al.
94. A national-scale hybrid model for enhanced streamflow estimation – consolidating a physically based hydrological model with long short-term memory (LSTM) networks J. Liu et al.
95. Enhancing the capabilities of large language models for API calls through knowledge graphs Y. Yang et al.
96. Skill-informed seamless communication of European S2S hydrological forecasts I. Pechlivanidis & L. Crochemore
97. Physics-aware machine learning revolutionizes scientific paradigm for process-based modeling in hydrology Q. Xu et al.
98. Machine learning-enhanced baseflow estimation from digital separation methods: insights from south-western Nigeria A. Awode et al.
99. National-scale climate forecasting in Türkiye using statistical and artificial intelligence-based machine learning models under CMIP6 scenarios H. Çiftçi et al.
100. Skilful probabilistic predictions of UK flood risk months ahead using a large-sample machine learning model trained on multimodel ensemble climate forecasts S. Moulds et al.
101. A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis A. Adombi et al.
102. Review article: Towards improved drought prediction in the Mediterranean region – modeling approaches and future directions B. Zellou et al.
103. Thermal Load Predictions in Low-Energy Buildings: A Hybrid AI-Based Approach Integrating Integral Feature Selection and Machine Learning Models Y. El Mghouchi & M. Udristioiu
104. Challenges and opportunities of ML and explainable AI in large-sample hydrology L. Slater et al.
105. Integrating AI in plant science: A review of applications and future prospects I. Khan & B. Khare
106. Impact-Based Decision Making in Flood Risk Management: A Review of Behavioral and Technical Approaches Z. Bovermann et al.
107. UK Hydrological Outlook using Historic Weather Analogues W. Chan et al.
108. Neural Emulators vs. Machine Learning Regressors: District‑Scale Pre‑Monsoon Temperature Prediction in Telangana G. Velusamy et al.
109. Evaluation of machine learning-dynamical hybrid method incorporating remote sensing data for in-season maize yield prediction under drought Y. Luo et al.
110. High‐resolution flood probability mapping using generative machine learning with large‐scale synthetic precipitation and inundation data L. Huang et al.
111. Future climate prediction and projection: A systematic review of classical and advanced methodologies R. Sepaspour et al.
112. Seasonal predictability of an indicator of mass coral bleaching between the Pacific ocean and the East China sea with a large ensemble climate model T. Doi et al.
113. The Implementation of Multimodal Large Language Models for Hydrological Applications: A Comparative Study of GPT-4 Vision, Gemini, LLaVa, and Multimodal-GPT L. Kadiyala et al.
114. Artificial intelligence and numerical weather prediction models: A technical survey M. Waqas et al.
115. Protocols for Water and Environmental Modeling Using Machine Learning in California M. He et al.
116. Rainy season precipitation trends over motloutse watershed in semi-arid eastern Botswana (1989–2019): implications on groundwater resources and sustainability G. Lentswe & L. Molwalefhe
117. Knowledge‐Guided Machine Learning for Global Change Ecology Research Z. Jin et al.
118. DAHG: A Dynamic Augmented Heterogeneous Graph Framework for Precipitation Forecasting with Incomplete Data H. Tang et al.
119. Critical science for the next decade of climate risk management M. Parsons et al.
120. A Deep Hybrid CNNDBiLSTM Model for Short-Term Wind Speed Forecasting in Wind-Rich Regions of Tasmania, Australia A. Neupane et al.
121. Прогнозне моделювання процесів формування паводкових стоків з використанням геоінформаційних технологій О. Кравець
122. A Weighted Likelihood Ensemble Approach for Failure Prediction of Water Pipes R. Beig Zali et al.
123. Analyzing the generalization capabilities of a hybrid hydrological model for extrapolation to extreme events E. Acuña Espinoza et al.
124. Multi-decadal streamflow projections for catchments in Brazil based on CMIP6 multi-model simulations and neural network embeddings for linear regression models M. Scheuerer et al.
125. Assessment of performance of conventional and machine learning methods for estimating missing precipitation data A. Poyam et al.
126. Consecutive one-week model predictions of land surface temperature stay on track for a decade with chaotic behavior tracking J. Ren et al.
127. Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods I. Mirpulatov et al.
128. A pioneering approach to deterministic rainfall forecasting for wet period in the Northern Territory of Australia using machine learning R. Farooq et al.
129. Hybrid approaches enhance hydrological model usability for local streamflow prediction Y. Du & I. Pechlivanidis
130. Assessing water security, climate risk, and groundwater resilience for sustainable development in the Eastern Province of Saudi Arabia M. Rahman et al.
131. Analysis of time series characteristics using machine learning model and correlation matrix in the tasks of forecasting the state of forest ecosystems P. Gusev et al.
132. A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia R. Taylor et al.
133. A Novel mRMR-RFE-RF Method for Enhancing Medium- and Long-Term Hydrological Forecasting: A Case Study of the Danjiangkou Basin T. Tang et al.
134. Soft computing paradigm for climate change adaptation and mitigation in Iran, Pakistan, and Turkey: A systematic review M. Talha et al.
135. CrackNet: A transformer-based approach for detecting microcrack in photovoltaic panels based on electroluminescence images H. Gao et al.
136. Temperature-based solar energy forecasting: a big data analysis for sustainable energy planning in Ishwardi and Rajshahi region of Bangladesh H. Alif
137. A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data W. Liu et al.
138. Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach Q. Yu et al.
139. Filling the Gaps: Creating a Consistent Rainfall Dataset for Maranhão State, Brazil (1987–2023) G. Reschke et al.