93 citations as recorded by crossref.

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32. Comparison of fuzzy inference systems for streamflow prediction M. ÖZGER 10.1623/hysj.54.2.261
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51. Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models Z. Pashaei & R. Dehkharghani 10.29252/jsdp.17.4.89
52. Spatiotemporal Groundwater Level Forecasting in Coastal Aquifers by Hybrid Artificial Neural Network-Geostatistics Model: A Case Study V. Nourani et al. 10.1089/ees.2010.0174
53. Mapping runoff based on hydro-stochastic approach for the Huaihe River Basin, China Z. Yan et al. 10.1007/s11442-011-0856-3
54. Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach M. Jamei et al. 10.1007/s11269-022-03270-6
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56. Physically Based and Data-Driven Models and Propagation of Input Uncertainties in River Flood Prediction R. Shrestha & F. Nestmann 10.1061/(ASCE)HE.1943-5584.0000123
57. Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
58. Drinking Water Source Contamination Early Warning System and Modelling in China: A Review A. Burchard-Levine et al. 10.11159/ijepr.2012.003
59. Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis Z. Zeng et al. 10.3390/su13020792
60. Modeling the Relationship between Catchment Attributes and In-stream Water Quality I. Fatehi et al. 10.1007/s11269-015-1103-y
61. LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios A. Ahmed et al. 10.1007/s00477-021-01969-3
62. Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach K. Wang & A. Altunkaynak 10.1061/(ASCE)HE.1943-5584.0000419
63. Prediction of seepage flow through earthfill dams using machine learning models I. Rehamnia et al. 10.1016/j.hydres.2024.01.005
64. A simulation of the rainfall-runoff process using artificial neural network and HEC-HMS model in forest lands V. Gholami & M. Khaleghi 10.17221/90/2020-JFS
65. Heuristic Methods for Reservoir Monthly Inflow Forecasting: A Case Study of Xinfengjiang Reservoir in Pearl River, China C. Cheng et al. 10.3390/w7084477
66. Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting M. Saharia & R. Bhattacharjya 10.1007/s12205-012-1463-2
67. Hybridization of ANFIS and fuzzy logic for groundwater quality assessment . Shwetank et al. 10.1016/j.gsd.2022.100777
68. Prediction of sea water levels using wind information and soft computing techniques S. Nitsure et al. 10.1016/j.apor.2014.07.003
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70. Predicting Water Level Fluctuations in Lake Van Using Hybrid Season-Neuro Approach A. Altunkaynak 10.1061/(ASCE)HE.1943-5584.0001804
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73. Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods A. Altunkaynak 10.1007/s11269-014-0616-0
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78. RETRACTED ARTICLE: Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India S. Samantaray et al. 10.1007/s00500-022-07097-6
79. Data fusion-based risk assessment framework: an example of benzene M. Islam et al. 10.1007/s13198-012-0136-3
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84. A time-varying parameter estimation approach using split-sample calibration based on dynamic programming X. Zhang & P. Liu 10.5194/hess-25-711-2021
85. Assessment of Artificial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia M. Ahooghalandari et al. 10.1080/13241583.2015.1116183
86. RETRACTED ARTICLE: Deep convolutional neural network to predict ground water level A. Zamani et al. 10.1007/s41324-023-00537-x
87. Prediction of Short-Term Operational Water Levels Using an Adaptive Neuro-Fuzzy Inference System J. Shiri et al. 10.1061/(ASCE)WW.1943-5460.0000097
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