Adnan RM, Liang Z, Heddam S, Zounemat-Kermani M, Kisi O and Li B (2020) Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs. Journal of Hydrology 586:124371
Bagheri Y, Abas novin pour E, Nadiri AA and Naderi K (2018) Forecasting of groundwater level fluctuations in Baruq aquifer using the SOM-AI model. Scientific Quarterly Journal 28(112):157-166 (In Persian)
Bai T and Tahmasebi P (2023) Graph neural network for groundwater level forecasting. Journal of Hydrology 616:128792
Bhagat SK, Tung TM, and Yaseen ZM (2020) Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research. Journal of Cleaner Production 250:119473
Cheng M, Jiao X, Jin X, Li B, Liu K, and Shi L (2021) Satellite time series data reveal interannual and seasonal spatiotemporal evapotranspiration patterns in China in response to effect factors. Agricultural Water Management 255:107046
Goodfellow I, Bengio Y, and Courville A (2017) Deep Learning. MIT Press 521(7553):785
Hou AY, Kakar RK, Neeck S, Azarbarzin AA, Kummerow CD, Kojima M, Oki R, Nakamura K, and Iguchi T (2014) The global precipitation measurement mission. Bulletin of the American Meteorological Society 95(5):701–722
Hu G, Jia L and Menenti M (2015) Comparison of MOD16 and LSA-SAF MSG evapotranspiration products over Europe for 2011. Remote Sensing of Environment156:510–526
Ketabchi, H, Mahmoodzadeh D, Valipour E, and Saadi T (2024) Uncertainty-based analysis of water balance components: A semi-arid groundwater-dependent and data-scarce area, Iran. Environment, Development and Sustainability 26:31511-31537
Khan J, Lee E, Balobaid AS, and Kim K (2023) A comprehensive review of conventional, machine leaning, and deep learning models for Groundwater Level (GWL) Forecasting. Applied Sciences (Switzerland) 13(4):2743
Khatibi R and Nadiri AA (2021) Inclusive Multiple Models (IMM) for predicting groundwater levels and treating heterogeneity. Geoscience Frontiers 12(2):713–724
Kong-A-Siou L, Fleury P, Johannet A, Borrell Estupina V, Pistre S, and Dörfliger N (2014) Performance and complementarity of two systemic models (reservoir and neural networks) used to simulate spring discharge and piezometry for a karst aquifer. Journal of Hydrology 519(PD):3178–3192
LeCun Y, Bengio Y, and Hinton G (2015) Deep learning. Nature 521(7553):436–444
Mahdavi-Meymand A and Zounemat-Kermani M (2020) A new integrated model of the group method of data handling and the firefly algorithm (GMDH-FA): Application to aeration modelling on spillways. Artificial Intelligence Review 53(4):2549–2569
Masoumi Z, Rezaei A, and Maleki J (2019) Improvement of water table interpolation and groundwater storage volume using fuzzy computations. Environmental Monitoring and Assessment 191(6):1–15
Nadiri AA, Vahedi F, Asghari Moghaddam A, and Kadkhodaie A (2016) Supervised intelligent committee machine method for groundwater level prediction. Journal of Civil and Environmental Engineering. Faculty of Natural Science, University of Tabriz 46(3):101-112 (In Persian)
Norozi H and Nadiri AA (2018) Predicting groundwater levels in the Bukan Plain using fuzzy logic, random forest and neural network models. Journal of Iranian Natural Resources 71(3):846-829 (In Persian)
Peifeng Li JZ and Krebs P (2022) Prediction of flow based on a CNN-LSTM combined deep. Water 14(6):993
Rajaee T, Ebrahimi H, and Nourani V (2019) A review of the artificial intelligence methods in groundwater level modeling. Journal of Hydrology 572:336–351
Rogers LL and Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling has been successfully applied to a variety of optimization. Water Resources Research 30(2):457–481
Ruhoff A, de Andrade BC, Laipelt L, Fleischmann AS, Siqueira VA, et al. (2022) Global evapotranspiration datasets assessment using water balance in South America. Remote Sensing 14(11):2526
Sadeghi-Jahani H, Ketabchi H, and Shafizadeh-Moghadam H (2024) Spatiotemporal assessment of sustainable groundwater management using process-based and remote sensing indices: A novel approach. Science of the Total Environment 918:170828
Sengupta S, Basak S, Saikia P, Paul S, Tsalavoutis V, Atiah F, Ravi V, and Peters A (2020) A review of deep learning with special emphasis on architectures, applications and recent trends. Knowledge-Based Systems 194:105596
Sit M, Demiray BZ, Xiang Z, Ewing GJ, Sermet Y, and Demir I (2020) A comprehensive review of deep learning applications in hydrology and water resources. Water Science and Technology 82(12):2635–2670
Tan ML and Duan Z (2017) Assessment of GPM and TRMM precipitation products over Singapore. Remote Sensing 9(7):1–16
Tao H, Hameed MM, Marhoon HA, Zounemat-Kermani M, Heddam S, et al. (2022) Groundwater level prediction using machine learning models: A comprehensive review. Neurocomputing 489:271–308
Trambauer P, Dutra E, Maskey S, Werner M, Pappenberger F, Van Beek LPH, and Uhlenbrook S (2014) Comparison of different evaporation estimates over the African continent. Hydrology and Earth System Sciences 18(1):193–212
Van Rossum G (1995) No Title. Available at: https://www.python.org/
Wambura FJ, Dietrich O, and Lischeid G (2017) Evaluation of spatio-temporal patterns of remotely sensed evapotranspiration to infer information about hydrological behaviour in a data-scarce region. Water (Switzerland) 9(5):333
Wang Z, Zhong R, Lai C, and Chen J (2017) Evaluation of the GPM IMERG satellite-based precipitation products and the hydrological utility. Atmospheric Research 196(May):151–163
Wunsch A, Liesch T, and Broda S (2021) Groundwater level forecasting with artificial neural networks: A comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX). Hydrology and Earth System Sciences 25(3):1671–1687
Yaseen ZM, Sulaiman SO, Deo RC, and Chau KW (2019) An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology 569:387–408