97 citations as recorded by crossref.

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55. Stock Market Modeling Using Artificial Neural Network and Comparison with Classical Linear Models Z. Pashaei & R. Dehkharghani 10.29252/jsdp.17.4.89
56. 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
57. Mapping runoff based on hydro-stochastic approach for the Huaihe River Basin, China Z. Yan et al. 10.1007/s11442-011-0856-3
58. 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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61. Optimising Fuzzy Neural Network Architecture for Dissolved Oxygen Prediction and Risk Analysis U. Khan & C. Valeo 10.3390/w9060381
62. Drinking Water Source Contamination Early Warning System and Modelling in China: A Review A. Burchard-Levine et al. 10.11159/ijepr.2012.003
63. Water Pollution and Its Causes in the Tuojiang River Basin, China: An Artificial Neural Network Analysis Z. Zeng et al. 10.3390/su13020792
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65. 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
66. Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach K. Wang & A. Altunkaynak 10.1061/(ASCE)HE.1943-5584.0000419
67. Prediction of seepage flow through earthfill dams using machine learning models I. Rehamnia et al. 10.1016/j.hydres.2024.01.005
68. 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
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82. RETRACTED ARTICLE: Prediction of groundwater fluctuation based on hybrid ANFIS-GWO approach in arid Watershed, India S. Samantaray et al. 10.1007/s00500-022-07097-6
83. Data fusion-based risk assessment framework: an example of benzene M. Islam et al. 10.1007/s13198-012-0136-3
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90. RETRACTED ARTICLE: Deep convolutional neural network to predict ground water level A. Zamani et al. 10.1007/s41324-023-00537-x
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