200 citations as recorded by crossref.

1. Application of generalized regression neural network and support vector regression for monthly rainfall forecasting in western Jilin Province, China W. Lu et al. https://doi.org/10.2166/aqua.2014.002
2. Optimal placement of water-level sensors to facilitate data-driven management of hydrological infrastructure assets in coastal mega-cities of developing nations R. Ogie et al. https://doi.org/10.1016/j.scs.2017.08.019
3. Improving monthly precipitation prediction accuracy using machine learning models: a multi-view stacking learning technique M. El Hafyani et al. https://doi.org/10.3389/frwa.2024.1378598
4. CNN-LSTM-AM model-based reliable prediction method for the meteorological wet bulb temperature J. Wang et al. https://doi.org/10.1016/j.dsp.2025.105724
5. Modeling and forecasting river flow rate from the Melen Watershed, Turkey M. Akiner & A. Akkoyunlu https://doi.org/10.1016/j.jhydrol.2012.06.031
6. Verification of probabilistic seasonal rainfall forecasts for Fiji Islands A. Pratap et al. https://doi.org/10.1007/s00477-025-03002-3
7. Using exogenous variables to improve precipitation predictions of ANNs in arid and hyper-arid climates H. Bari Abarghouei & S. Hosseini https://doi.org/10.1007/s12517-016-2679-0
8. Evaluation of input variable selection methods in artificial neural networks for estimating missing daily precipitation M. Ghodrati & A. Dariane https://doi.org/10.1080/02626667.2024.2387156
9. Crowdsourced Predictive Analysis for the International Trade of South Indian Coir Products in Ayurveda, Textile and Agriculture R. S & R. Sasikala https://doi.org/10.2139/ssrn.3355103
10. Assessment of climate change in Upper Jhelum Sub-catchment, India, using nonparametric methods and random forest model R. Ali et al. https://doi.org/10.1007/s11600-024-01505-1
11. Forecasting of Rainfall Using Time Delay Neural Network in Tizi-Ouzou (Algeria) K. Benmahdjoub et al. https://doi.org/10.1016/j.egypro.2013.07.129
12. Annual precipitation forecast for west, southwest, and south provinces of Iran using artificial neural networks S. Azadi & A. Sepaskhah https://doi.org/10.1007/s00704-011-0575-9
13. Neural Network Forecasting of Precipitation Volumes Using Patterns A. Gorshenin & V. Kuzmin https://doi.org/10.1134/S1054661818030069
14. Data-driven multi-time-step ahead daily rainfall forecasting using singular spectrum analysis-based data pre-processing P. Unnikrishnan & V. Jothiprakash https://doi.org/10.2166/hydro.2017.029
15. Lightning Forecasting in Pre-Monsoon Season Using Non-Linear Autoregressive Artificial Neural Network P. Upadhyay et al. https://doi.org/10.1007/s00024-024-03654-2
16. Modeling and simulation of OBPN for forecasting of long-range monsoon AWR over a smaller homogeneous region through astrological parameters and its verification for 2019 S. Karmakar & S. Goswami https://doi.org/10.1007/s42044-019-00051-0
17. Estimation of Precipitable Water Vapor from the Radiometer at the Ali Observatory, Tibet X. Qian et al. https://doi.org/10.1088/1538-3873/ae7cb5
18. VERİ ZARFLAMA ANALİZİ TEMELLİ YAPAY SİNİR AĞLARI VE LOJİSTİK REGRESYON ANALİZİ İLE TEKNOLOJİ GELİŞTİRME BÖLGELERİNİN ETKİNLİKLERİNİN TAHMİNLENMESİ E. ÇAKIN & A. ÖZDEMİR https://doi.org/10.17065/huniibf.414156
19. Artificial neural networks in temporal and spatial variability studies and prediction of rainfall M. Richard & K. Gopal Rao https://doi.org/10.1080/09715010.2013.806400
20. Concept Drift Detection in Data Stream Clustering and its Application on Weather Data . Namitha K. & . Santhosh Kumar G. https://doi.org/10.4018/IJAEIS.2020010104
21. Artificial Intelligence Based Ensemble Modeling for Multi-Station Prediction of Precipitation V. Nourani et al. https://doi.org/10.3390/atmos10020080
22. Spatiotemporal Pattern Networks of Heavy Rain among Automatic Weather Stations and Very-Short-Term Heavy-Rain Prediction Y. Kim & Y. Yoon https://doi.org/10.1155/2016/4063632
23. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique A. Nair et al. https://doi.org/10.1007/s00024-017-1652-5
24. Simulation of precipitation time series using tree-rings, earlywood vessel features, and artificial neural network V. Gholami et al. https://doi.org/10.1007/s00704-018-2702-3
25. Dynamics of COVID-19 in India using WANFIS (Wavelet Adaptive Neuro-Fuzzy Inference System) model S. Singh et al. https://doi.org/10.1186/s12982-025-00670-y
26. A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. https://doi.org/10.5194/hess-16-4417-2012
27. Estimation of Intensity-Duration-Frequency (IDF) Curves from Large Scale Atmospheric Dataset by Statistical Downscaling K. ALRAMLAWİ & O. FISTIKOĞLU https://doi.org/10.18400/tekderg.874035
28. Increasing Neurons or Deepening Layers in Forecasting Maximum Temperature Time Series? T. Tran et al. https://doi.org/10.3390/atmos11101072
29. Enhancing daily rainfall prediction in urban areas: a comparative study of hybrid artificial intelligence models with optimization algorithms Y. Sheikhi et al. https://doi.org/10.1007/s13201-023-02036-8
30. Weather Forecasting Model using Artificial Neural Network K. Abhishek et al. https://doi.org/10.1016/j.protcy.2012.05.047
31. Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting Ö. Baydaroğlu & I. Demir https://doi.org/10.2166/hydro.2024.235
32. Long-term precipitation prediction in different climate divisions of California using remotely sensed data and machine learning S. Majnooni et al. https://doi.org/10.1080/02626667.2023.2248112
33. Hybrid Wavelet-Postfix-GP Model for Rainfall Prediction of Anand Region of India V. Dabhi & S. Chaudhary https://doi.org/10.1155/2014/717803
34. Long-Term Prediction of Hydrometeorological Time Series Using a PSO-Based Combined Model Composed of EEMD and LSTM G. Wu et al. https://doi.org/10.3390/su151713209
35. Comparison of hybrid machine learning model for the analysis of black carbon in air around the major coal mines of India S. Makkhan et al. https://doi.org/10.1007/s00521-022-07909-8
36. Evaluating the impact and risk of pluvial flash flood on intra-urban road network: A case study in the city center of Shanghai, China J. Yin et al. https://doi.org/10.1016/j.jhydrol.2016.03.037
37. Snow glacier melt estimation in tropical Andean glaciers using artificial neural networks V. Moya Quiroga et al. https://doi.org/10.5194/hess-17-1265-2013
38. Application of ANN and ANFIS Models on Dryland Precipitation Prediction (Case Study: Yazd in Central Iran) M. Dastorani et al. https://doi.org/10.3923/jas.2010.2387.2394
39. Hybrid artificial intelligence models based on adaptive neuro fuzzy inference system and metaheuristic optimization algorithms for prediction of daily rainfall B. Pham et al. https://doi.org/10.1016/j.pce.2024.103563
40. Rainfall Forecasting Using Various Artificial Neural Network Techniques - A Review N. Thakur et al. https://doi.org/10.32628/CSEIT2173159
41. Using the mutual information technique to select explanatory variables in artificial neural networks for rainfall forecasting M. Babel et al. https://doi.org/10.1002/met.1495
42. ANN and GRNN-Based Coupled Model for Flood Inundation Mapping of the Punpun River Basin S. Ranjan & V. Singh https://doi.org/10.48084/etasr.5483
43. Performance comparison of artificial neural network models for daily rainfall prediction S. Devi et al. https://doi.org/10.1007/s11633-016-0986-2
44. Real-time reservoir operation using recurrent neural networks and inflow forecast from a distributed hydrological model S. Yang et al. https://doi.org/10.1016/j.jhydrol.2019.124229
45. SUITABILITY OF NEURAL NETWORK TECHNIQUES FOR RAINFALL-RUNOFF MODELLING OVER A RIVER BASIN: A COMPREHENSIVE LITERATURE REVIEW K. SANJEEV et al. https://doi.org/10.26634/jfet.13.4.14434
46. Design of a flood magnitude prediction model using algorithmic and mathematical approaches A. Simeon et al. https://doi.org/10.1007/s41870-021-00706-x
47. Incorporating marine particulate carbon into machine learning for accurate estimation of coastal chlorophyll-a J. Niu et al. https://doi.org/10.1016/j.marpolbul.2023.115089
48. Development of advanced artificial intelligence models for daily rainfall prediction B. Pham et al. https://doi.org/10.1016/j.atmosres.2020.104845
49. Integrated Artificial Intelligent with RS and GIS for Long-Term Drought Prediction B. Yahya et al. https://doi.org/10.1088/1757-899X/1152/1/012024
50. Prediction of monthly precipitation using various artificial models and comparison with mathematical models Y. Kassem et al. https://doi.org/10.1007/s11356-022-24912-7
51. Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment J. Estévez et al. https://doi.org/10.3390/w12071909
52. Groundwater Level Prediction Using Artificial Neural Networks: A Case Study in Tra Noc Industrial Zone, Can Tho City, Vietnam T. Ty et al. https://doi.org/10.4236/jwarp.2018.109050
53. Improving Monthly Rainfall Forecast in a Watershed by Combining Neural Networks and Autoregressive Models A. Pérez-Alarcón et al. https://doi.org/10.1007/s40710-022-00602-x
54. Feasibility of Different Weather Data Sources Applied to Building Indoor Temperature Estimation Using LSTM Neural Networks M. Pensado-Mariño et al. https://doi.org/10.3390/su132413735
55. A hybrid EMD and MODWT models for monthly precipitation forecasting using an innovative error decomposition method L. Parviz & M. Ghorbanpour https://doi.org/10.1007/s00477-024-02797-x
56. Automated rain fall prediction enabled by optimized convolutional neural network‐based feature formation with adaptive long short‐term memory framework A. K et al. https://doi.org/10.1002/cpe.6868
57. Precipitation forecasting by using wavelet-support vector machine conjunction model O. Kisi & M. Cimen https://doi.org/10.1016/j.engappai.2011.11.003
58. Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three-Dimensional Hydrodynamic Models W. Chen et al. https://doi.org/10.4236/cweee.2017.61009
59. Neuro-fuzzy-wavelet hybrid approach to estimate the future trends of river water quality K. Parmar et al. https://doi.org/10.1007/s00521-019-04560-8
60. Real-time multi-step-ahead water level forecasting by recurrent neural networks for urban flood control F. Chang et al. https://doi.org/10.1016/j.jhydrol.2014.06.013
61. A Comparison of Regularization Techniques in Deep Neural Networks I. Nusrat & S. Jang https://doi.org/10.3390/sym10110648
62. Modelling spatial, altitudinal and temporal variability of annual precipitation in mountainous regions: The case of the Middle Zagros, Iran R. Saeidabadi et al. https://doi.org/10.1007/s13143-016-0026-8
63. Monthly rainfall forecasting for southern Nigeria using 2D deep learning and implications for climate adaptation and sustainability: A CNN-LSTM+Attention approach V. Nwazelibe et al. https://doi.org/10.1016/j.pce.2026.104577
64. ANN-RBF Hybrid Model for Spatiotemporal Estimation of Monthly Precipitation Case Study V. Nourani et al. https://doi.org/10.4018/jamc.2013040101
65. Climatic data analysis for groundwater level simulation in drought prone Barind Tract, Bangladesh: Modelling approach using artificial neural network R. Hasda et al. https://doi.org/10.1016/j.gsd.2020.100361
66. Prediction of Monthly Rainfall in Togo Using Artificial Neural Networks (ANN) L. Batablinlè et al. https://doi.org/10.4236/ojapps.2025.159190
67. ÜLKELERİN İNOVASYON PERFORMANSININ ÖLÇÜLMESİNDE YAPAY SİNİR AĞLARI, BULANIK DEMATEL TABANLI ANALİTİK AĞ SÜRECİ VE AĞIRLIK KISITLI VERİ ZARFLAMA ANALİZİ YAKLAŞIMLARININ BÜTÜNLEŞİK OLARAK KULLANILMASI VE BİR UYGULAMA E. ÇAKIN & A. ÖZDEMİR https://doi.org/10.24889/ifede.839778
68. Flash flood warnings using the ensemble precipitation forecasting technique: A case study on forecasting floods in Taiwan caused by typhoons T. Yang et al. https://doi.org/10.1016/j.jhydrol.2014.11.028
69. Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data V. Jothiprakash & R. Magar https://doi.org/10.1016/j.jhydrol.2012.04.045
70. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok? M. Vu et al. https://doi.org/10.1007/s00704-015-1580-1
71. Incorporating large-scale atmospheric variables in long-term seasonal rainfall forecasting using artificial neural networks: an application to the Ping Basin in Thailand M. Babel et al. https://doi.org/10.2166/nh.2016.212
72. Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts M. Ali et al. https://doi.org/10.1016/j.jhydrol.2020.124647
73. Quality Monitoring Research of Small Scale Resistance Spot Welding Based on Voltage Signal D. Zhao et al. https://doi.org/10.2355/isijinternational.53.240
74. Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach U. Misra et al. https://doi.org/10.1155/2018/3053640
75. Is Deep Better in Extreme Temperature Forecasting? T. Tran & T. Lee https://doi.org/10.9798/KOSHAM.2019.19.7.55
76. Estimation of effective diffusion coefficient of radionuclides in bentonite by machine learning method S. Gong et al. https://doi.org/10.1016/j.anucene.2025.111223
77. Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation J. Seo et al. https://doi.org/10.1155/2014/203545
78. Examining the Applicability of Wavelet Packet Decomposition on Different Forecasting Models in Annual Rainfall Prediction H. Wang et al. https://doi.org/10.3390/w13151997
79. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al. https://doi.org/10.5194/hess-16-1151-2012
80. An improved approach to Arabic news classification based on hyperparameter tuning of machine learning algorithms I. Jamaleddyn et al. https://doi.org/10.1016/j.jer.2023.100061
81. Development of Precipitation Forecast Model Based on Artificial Intelligence and Subseasonal Clustering L. Parviz & K. Rasouli https://doi.org/10.1061/(ASCE)HE.1943-5584.0001862
82. Univariate Time Series Forecasting of Temperature and Precipitation with a Focus on Machine Learning Algorithms: a Multiple-Case Study from Greece G. Papacharalampous et al. https://doi.org/10.1007/s11269-018-2155-6
83. Deep Learning-Based Maximum Temperature Forecasting Assisted with Meta-Learning for Hyperparameter Optimization T. Thi Kieu Tran et al. https://doi.org/10.3390/atmos11050487
84. A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions H. Yoon et al. https://doi.org/10.1016/j.cageo.2016.03.002
85. Analysis of rainfall depth based on climatology conditions using artificial neural networks V. Dermawan & Y. Alfahnie https://doi.org/10.1088/1755-1315/437/1/012020
86. Monthly rainfall forecasting using deep learning methods in big databases: a case study of northwestern Iran Z. Masoumi et al. https://doi.org/10.1007/s00382-025-07853-y
87. A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran O. Hamidi et al. https://doi.org/10.1007/s00704-014-1141-z
88. Artificial intelligence models for prediction of monthly rainfall without climatic data for meteorological stations in Ethiopia W. Abebe & D. Endalie https://doi.org/10.1186/s40537-022-00683-3
89. Weather Forecasting Using Artificial Neural Network (ANN): A Review O. ZEMNAZI et al. https://doi.org/10.1016/j.procs.2024.08.090
90. Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall Q. Pham et al. https://doi.org/10.1007/s11269-019-02408-3
91. A systematic literature review of deep learning-based text summarization: Techniques, input representation, training strategies, mechanisms, datasets, evaluation, and challenges M. Saleh et al. https://doi.org/10.1016/j.eswa.2024.124153
92. Deterministic weather forecasting models based on intelligent predictors: A survey K. Jaseena & B. Kovoor https://doi.org/10.1016/j.jksuci.2020.09.009
93. Increased Occurrence and Intensity of Consecutive Rainfall Events in the China's Three Gorges Reservoir Area Under Global Warming Y. Zheng et al. https://doi.org/10.1029/2020EA001188
94. Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites https://doi.org/10.5194/hess-14-1309-2010
95. A GA-XGBoost qualitative model for short-term rainfall forecasting based on GNSS PWV and meteorological parameters X. Yujing et al. https://doi.org/10.1016/j.jastp.2026.106938
96. Improving the accuracy of flood forecasting for Northeast China by the correction of global forecast rainfall based on deep learning S. Chen et al. https://doi.org/10.1016/j.jhydrol.2024.131733
97. Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations A. Najah et al. https://doi.org/10.5194/hess-15-2693-2011
98. Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models A. Khaniani et al. https://doi.org/10.1016/j.jastp.2020.105533
99. Modeling prediction of dispersal of heavy metals in plain using neural network S. Boudaghpour & S. Malekmohammadi https://doi.org/10.1080/23249676.2020.1719219
100. Long-term rainfall forecasting in arid climates using artificial intelligence and statistical recurrent models A. Alsumaiei https://doi.org/10.1016/j.jer.2024.03.001
101. Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal–ordinal support vector classifier J. Sánchez-Monedero et al. https://doi.org/10.1016/j.engappai.2014.05.016
102. An inverse-problem approach to detect outliers in rainfall measurements of ground gauges for robust reservoir flood control operation C. Wu & F. Chou https://doi.org/10.1016/j.jhydrol.2023.129360
103. Ensemble of evolving data clouds and fuzzy models for weather time series prediction E. Soares et al. https://doi.org/10.1016/j.asoc.2017.12.032
104. Rainfall Data Modeling with Artificial Neural Networks Approach S. Astutik et al. https://doi.org/10.1088/1742-6596/2123/1/012029
105. ÜLKELERİN İNOVASYON PERFORMANSININ ÖLÇÜLMESİNDE YAPAY SİNİR AĞLARI, BULANIK DEMATEL TABANLI ANALİTİK AĞ SÜRECİ VE AĞIRLIK KISITLI VERİ ZARFLAMA ANALİZİ YAKLAŞIMLARININ BÜTÜNLEŞİK OLARAK KULLANILMASI VE BİR UYGULAMA E. Çakın & A. Özdemir https://doi.org/10.24889/ifede.513623
106. Monthly Maximum Accumulated Precipitation Forecasting Using Local Precipitation Data and Global Climate Modes J. Sulaiman et al. https://doi.org/10.20965/jaciii.2014.p0999
107. Hydroclimate variability and long-lead forecasting of rainfall over Thailand by large-scale atmospheric variables N. Singhrattna et al. https://doi.org/10.1080/02626667.2011.633916
108. Assessing the vulnerability of hydrological infrastructure to flood damage in coastal cities of developing nations R. Ogie et al. https://doi.org/10.1016/j.compenvurbsys.2017.11.004
109. A multi-step strategy for enhancing the rainfall-runoff modeling: combination of lumped and artificial intelligence-based hydrological models B. Mohammadi et al. https://doi.org/10.1007/s10668-025-06743-x
110. River Water Prediction Modeling Using Neural Networks, Fuzzy and Wavelet Coupled Model K. Parmar & R. Bhardwaj https://doi.org/10.1007/s11269-014-0824-7
111. Rainfall Prediction with AMSR–E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran M. Fereidoon & M. Koch https://doi.org/10.3390/w10070964
112. Tollgate neural networks (TNN) model with time bound learning methodology for futuristic approach in climatic data analysis A. Raj et al. https://doi.org/10.1007/s40808-018-0495-0
113. Temporal Disaggregation and Short-Term Forecasting of Hourly Rainfall Data: A Case Study of Raipur City Chhattisgarh, India G. Patel et al. https://doi.org/10.1007/s40996-025-01901-7
114. Predictor selection method for the construction of support vector machine (SVM)‐based typhoon rainfall forecasting models using a non‐dominated sorting genetic algorithm T. Yang et al. https://doi.org/10.1002/met.1717
115. Prediction of Building’s Thermal Performance Using LSTM and MLP Neural Networks M. Martínez Comesaña et al. https://doi.org/10.3390/app10217439
116. Long-Short Term Memory Technique for Monthly Rainfall Prediction in Thale Sap Songkhla River Basin, Thailand N. Salaeh et al. https://doi.org/10.3390/sym14081599
117. Shear Capacity Prediction of Extremely-Loaded Box Culvert on Elastic Soil Using Artificial Neural Network Y. Tuskan & D. Yıldırım Uncu https://doi.org/10.16984/saufenbilder.1281213
118. Forecasting rainfall using transfer entropy coupled directed–weighted complex networks H. Tongal & B. Sivakumar https://doi.org/10.1016/j.atmosres.2021.105531
119. ERA5–NASA Ensembles for Daily Rain Prediction Supporting Irrigation in Konya, Türkiye A. Çetinkaya https://doi.org/10.31015/2025.si.23
120. Precipitation forecast with logistics regression methods for harvest optimization M. SAMASTI & T. KÜÇÜKDENİZ https://doi.org/10.31015/jaefs.2023.1.26
121. Multi-source rainfall merging and reservoir inflow forecasting by ensemble technique and artificial intelligence Y. Chiang et al. https://doi.org/10.1016/j.ejrh.2022.101204
122. Improvement in District Scale Heavy Rainfall Prediction Over Complex Terrain of North East India Using Deep Learning O. Sharma et al. https://doi.org/10.1109/TGRS.2023.3322676
123. A prediction and evaluation system of the impact factors on the performance of the aerostatic slider D. Chen et al. https://doi.org/10.1016/j.rcim.2017.09.016
124. Precipitation Modeling by Polyhedral RCMARS and Comparison with MARS and CMARS A. Özmen et al. https://doi.org/10.1007/s10666-014-9404-8
125. Multi-step rainfall forecasting using deep learning approach S. Narejo et al. https://doi.org/10.7717/peerj-cs.514
126. Spatial Interpolation of GNSS Troposphere Wet Delay by a Newly Designed Artificial Neural Network Model M. Selbesoglu https://doi.org/10.3390/app9214688
127. Groundwater level as an input to monthly predicting of water level using various machine learning algorithms M. Sapitang et al. https://doi.org/10.1007/s12145-021-00654-x
128. Enhancing forecast reliability through formal verification: A model checking approach for weather prediction models A. Rashid et al. https://doi.org/10.1016/j.jer.2026.07.002
129. Multiple data fusion for rainfall estimation using a NARX-based recurrent neural network – the development of the REIINN model M. Ang et al. https://doi.org/10.1088/1755-1315/17/1/012019
130. Accurate precipitation prediction with support vector classifiers: A study including novel predictive variables and observational data E. Ortiz-García et al. https://doi.org/10.1016/j.atmosres.2014.01.012
131. ATFcast: adaptive time-frequency fusion model for rainfall forecasting using GNSS and meteorological data C. Xu et al. https://doi.org/10.1007/s10291-026-02066-0
132. ST-GRF: Spatiotemporal graph neural networks for rainfall forecasting F. Zhang & Z. Shao https://doi.org/10.1016/j.dsp.2023.103989
133. Estimation of monthly and seasonal precipitation: A comparative study using data-driven methods versus hybrid approach M. Taghi Sattari et al. https://doi.org/10.1016/j.measurement.2020.108512
134. Spatio-topological network analysis of hydrological infrastructure as a decision support tool for flood mitigation in coastal mega-cities R. Ogie et al. https://doi.org/10.1177/0265813516637608
135. Matrix Approach for the Seasonal Infectious Disease Spread Prediction H. HIROSE et al. https://doi.org/10.1587/transfun.E98.A.2010
136. Performance assessment of rainfall forecasting models for urban Guwahati City using machine learning techniques and singular spectrum analysis S. Priya Ashok & S. Pekkat https://doi.org/10.2166/wcc.2024.465
137. Prediction of heavy rainfall days over a peninsular Indian station using the machine learning algorithms K. Subrahmanyam et al. https://doi.org/10.1007/s12040-021-01725-9
138. Optimizing hyperparameters of deep hybrid learning for rainfall prediction: a case study of a Mediterranean basin A. Elbeltagi et al. https://doi.org/10.1007/s12517-022-10098-2
139. An investigation on the relationship between the Hurst exponent and the predictability of a rainfall time series S. Chandrasekaran et al. https://doi.org/10.1002/met.1784
140. Data-driven approaches outperform dynamical models for subseasonal-to-seasonal precipitation forecasting in Senegal D. Faye et al. https://doi.org/10.3389/fclim.2026.1812437
141. River flow time series using least squares support vector machines R. Samsudin et al. https://doi.org/10.5194/hess-15-1835-2011
142. One‐kilometer resolution forecasts of hourly precipitation over China using machine learning models B. Li et al. https://doi.org/10.1002/asl.1297
143. The tidal trap — Seasonal transport of floating macrodebris in the bi-directional Chao Phraya River network on the Gulf of Thailand T. Mani et al. https://doi.org/10.1016/j.marpolbul.2025.117605
144. Evaluation of climate change in northern Iran during the last four centuries by using dendroclimatology V. Gholami et al. https://doi.org/10.1007/s11069-016-2667-4
145. Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network A. Haidar & B. Verma https://doi.org/10.1109/ACCESS.2018.2880044
146. Decomposition genetic programming: An extensive evaluation on rainfall prediction in the context of weather derivatives S. Cramer et al. https://doi.org/10.1016/j.asoc.2018.05.016
147. A neural network-based local rainfall prediction system using meteorological data on the Internet: A case study using data from the Japan Meteorological Agency T. Kashiwao et al. https://doi.org/10.1016/j.asoc.2017.03.015
148. A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model S. Mehdizadeh et al. https://doi.org/10.1016/j.jhydrol.2017.09.056
149. Radar Vertical Profile of Reflectivity Correction with TRMM Observations Using a Neural Network Approach Y. Wang et al. https://doi.org/10.1175/JHM-D-14-0136.1
150. Prediction of uranium adsorption capacity on biochar by machine learning methods T. Da et al. https://doi.org/10.1016/j.jece.2022.108449
151. Comparative performance of wavelet-based neural network approaches P. Anjoy & R. Paul https://doi.org/10.1007/s00521-017-3289-9
152. SMOTE and ExtraTreesRegressor based random forest technique for predicting Australian rainfall M. Irshada & V. Kumar https://doi.org/10.1007/s41870-023-01185-y
153. Streamflow forecasting using least-squares support vector machines A. Shabri & . Suhartono https://doi.org/10.1080/02626667.2012.714468
154. A comparison of three prediction models for predicting monthly precipitation in Liaoyuan city, China J. Luo et al. https://doi.org/10.2166/ws.2016.006
155. Neural Network Approach to Forecast Hourly Intense Rainfall Using GNSS Precipitable Water Vapor and Meteorological Sensors P. Benevides et al. https://doi.org/10.3390/rs11080966
156. An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives S. Cramer et al. https://doi.org/10.1016/j.eswa.2017.05.029
157. Eigen time series modeling: a breakthrough approach to spatio-temporal rainfall forecasting in basins K. Küllahcı & A. Altunkaynak https://doi.org/10.1007/s00521-024-10864-1
158. Analysis of rainfall and large-scale predictors using a stochastic model and artificial neural network for hydrological applications in southern Africa P. Kenabatho et al. https://doi.org/10.1080/02626667.2015.1040021
159. An artificial neural network-based snow cover predictive modeling in the higher Himalayas B. Mishra et al. https://doi.org/10.1007/s11629-014-2985-5
160. Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data M. Hossain et al. https://doi.org/10.3390/hydrology9060111
161. Type 2 Fuzzy Inference-Based Time Series Model N. Rahim et al. https://doi.org/10.3390/sym11111340
162. The Application of Decision Making Models in Sustainable Management of Environmental Systems A. Shobeirinejad et al. https://doi.org/10.1080/09720510.2015.1047571
163. Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China Z. Xie et al. https://doi.org/10.1007/s11069-015-1648-3
164. Annual and Seasonal Rainfall Trends in an Equatorial Tropical River Basin in Malaysian Borneo M. Ninu Krishnan et al. https://doi.org/10.1007/s10666-018-9649-8
165. Artificial neural network for forecasting rainfall pattern in Prince Edward Island, Canada S. Khedhiri https://doi.org/10.1080/00207233.2014.961311
166. A Review on ANN Based Model for Solar Radiation and Wind Speed Prediction with Real-Time Data P. Malik et al. https://doi.org/10.1007/s11831-021-09687-3
167. Regional Analysis of Flow Duration Curves through Support Vector Regression M. Vafakhah & S. Khosrobeigi Bozchaloei https://doi.org/10.1007/s11269-019-02445-y
168. An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach X. Wang et al. https://doi.org/10.1080/02626667.2021.1937631
169. Deep BLSTM-GRU Model for Monthly Rainfall Prediction: A Case Study of Simtokha, Bhutan M. Chhetri et al. https://doi.org/10.3390/rs12193174
170. Time series forecasting model for non-stationary series pattern extraction using deep learning and GARCH modeling H. Han et al. https://doi.org/10.1186/s13677-023-00576-7
171. Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting M. Ali et al. https://doi.org/10.1016/j.atmosres.2018.07.005
172. Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection T. Kim et al. https://doi.org/10.1029/2019WR026262
173. Climate change impacts on the streamflow of Zarrineh River, Iran F. Yazdandoost & S. Moradian https://doi.org/10.1007/s40333-021-0091-4
174. Complex network analysis and robustness evaluation of spatial variation of monthly rainfall H. Tongal & B. Sivakumar https://doi.org/10.1007/s00477-023-02578-y
175. Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach M. Ali et al. https://doi.org/10.1080/02626667.2020.1808219
176. Precipitation pattern modeling using cross-station perception: regional investigation S. Sulaiman et al. https://doi.org/10.1007/s12665-018-7898-0
177. Integrating experimental-based vulnerability mapping with intelligent identification of multi-aquifer groundwater salinization M. Yassin et al. https://doi.org/10.1016/j.nxsust.2025.100115
178. Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction S. Meshram et al. https://doi.org/10.1007/s11269-020-02672-8
179. Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia H. Tamiru & M. Wagari https://doi.org/10.1007/s40808-021-01175-8
180. Trend Analysis of Water Inflow Into the Dam Reservoirs Under Future Conditions Predicted By Dynamic NAR and NARX Models P. Pishgah Hadiyan et al. https://doi.org/10.1007/s11269-022-03170-9
181. Performance of conceptual and black-box models in flood warning systems M. Banihabib & R. Revelli https://doi.org/10.1080/23311916.2015.1127798
182. Stochastic model genetic programming: Deriving pricing equations for rainfall weather derivatives S. Cramer et al. https://doi.org/10.1016/j.swevo.2019.01.008
183. Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation M. Ahuna et al. https://doi.org/10.23919/SAIEE.2019.8643146
184. Rainy Day Prediction Model with Climate Covariates Using Artificial Neural Network MLP, Pilot Area: Central Italy M. Gentilucci & G. Pambianchi https://doi.org/10.3390/cli10080120
185. Investigation of Kelvin wave periods during Hai-Tang typhoon using Empirical Mode Decomposition P. Kishore et al. https://doi.org/10.1016/j.jastp.2017.07.025
186. Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach M. Akiner & P. Nastos https://doi.org/10.1155/2021/5524611
187. A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands V. Gholami & M. Khaleghi https://doi.org/10.17221/90/2020-JFS
188. Combined Quantile Forecasting for High‐Dimensional Non‐Gaussian Data S. Park et al. https://doi.org/10.1002/env.70035
189. RETRACTED ARTICLE: Classification of rubberized coir fibres using deep learning-based neural fuzzy decision tree approach S. Remya & R. Sasikala https://doi.org/10.1007/s00500-019-03961-0
190. Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS T. Kim et al. https://doi.org/10.1016/j.jhydrol.2021.126423
191. Annual and Non-Monsoon Rainfall Prediction Modelling Using SVR-MLP: An Empirical Study From Odisha X. Zhang et al. https://doi.org/10.1109/ACCESS.2020.2972435
192. Coupling dynamical and statistical downscaling for high-resolution rainfall forecasting: case study of the Red River Delta, Vietnam Q. Tran Anh & K. Taniguchi https://doi.org/10.1186/s40645-018-0185-6
193. Modeling of monthly rainfall and runoff of Urmia lake basin using “feed-forward neural network” and “time series analysis” model J. Farajzadeh et al. https://doi.org/10.1016/j.wri.2014.10.003
194. A Relative Analysis of Modern ML Methods for Rainfall Prediction A. Huria et al. https://doi.org/10.51483/IJDSBDA.2.1.2022.10-19
195. Application of artificial neural network for forecasting standardized precipitation and evapotranspiration index: A case study of Nigeria A. Ogunrinde et al. https://doi.org/10.1002/eng2.12194
196. Spatiotemporal precipitation modeling by artificial intelligence-based ensemble approach V. Nourani et al. https://doi.org/10.1007/s12665-019-8755-5
197. Virtual weather stations for meteorological data estimations B. Franco et al. https://doi.org/10.1007/s00521-020-04727-8
198. Synergistic approach for streamflow forecasting in a glacierized catchment of western Himalaya using earth observation and machine learning techniques J. Dharpure et al. https://doi.org/10.1007/s12145-024-01322-6
199. Bagging Machine Learning Algorithms: A Generic Computing Framework Based on Machine-Learning Methods for Regional Rainfall Forecasting in Upstate New York N. Yu & T. Haskins https://doi.org/10.3390/informatics8030047
200. Employing the generalized Pareto distribution to analyze extreme rainfall events on consecutive rainy days in Thailand's Chi watershed: implications for flood management T. Phoophiwfa et al. https://doi.org/10.5194/hess-28-801-2024