Ahmadi F (2019) Evaluation of the performance of support vector machine methods and adaptive neural-fuzzy inference system in predicting the monthly flow of rivers (Case study: Nazlo and Caesar rivers. Research on Water and Soil Resources of Iran (Iranian Sciences and Agriculture) 51(3):673-686 (In Persian)
Ahmed AM, Deo RC, Ghahramani A, Feng Q, Raj N, Yin Z, and Yang L (2022) New double decomposition deep learning methods for river water level forecasting. Science of the Total Environment 831:154722
Baek SS, Pyo J, and Chun JA (2020) Prediction of water level and water quality using a CNN-LSTM combined deep learning approach. Water 1(12):3399
Bai Y, Bezak N, Zeng B, Li C, Sapač K, and Zhang J (2021) Daily runoff forecasting using a cascade long short-term memory model that considers different variables. Water Resources Management 35(4):1167-1181
Bennett T (1998) Development and application of a continuous soil moisture accounting algorithm for the Hydrologic Engineering Center-Hydrologic Modeling System HEC-HMS. MS Thesis, Department of civil and environmental engineering, University of, Davis, California
Biglerian A, Hagizadeh A, and Kazemnejad A (2009) Prediction of incomplete data using artificial neural network model. Journal of Basic Sciences of JSIAU, Islamic Azad University 20(78.2):21-28 (In Persian)
Coulibaly P (2010) Reservoir computing approach to Great Lakes water level forecasting. Journal of Hydrology 381:76–88
Dehghani N, Vafakhah M, and Bahremand AR (2016) Precipitation-runoff modeling using artificial neural network and adaptive neural fuzzy network in Kesilian watershed. Watershed Management Research 7(13):128-137 (In Persian)
Drewil GI, and AL-bahadili RJ (2022) Air pollution prediction using LSTM deep learning and metaheuristics algorithm. Measurement: Sensors, 100546
Godarzi MR, and Godarzi H (2020) Investigating the effectiveness of data group classification method and wavelet transformation in runoff forecasting (study area: Qorso watershed). Scientific Research Journal of Irrigation and Water Engineering of Iran 10(4):67-81 (In Persian)
Han H, Choi C, Jung J, and Kim HS (2021) Deep learning with long short-term memory based Sequence-to-Sequence model for Rainfall-Runoff simulation. Water 13(4):437
HEC (2008) HEC-HMS, User’s manual version 3.3. Hydrologic engineering center, California
Ivakhnenko AG (1971) Polynomial theory of complex systems. IEEE Transactions on Systems. Man, and Cybernetics, SMC-1(4):364-378
Jaefari M, Vafakhah M, and Abghari H (2013) Comparison of the performance of two sigmoid and hyperbolic tangent functions of artificial neural network in predicting the runoff coefficient of torrential rain (case study: Arie Neishabur floodplain). Water and soil Conservation Research (Agricultural Sciences and Natural Resources) 20(2):85-103 (In Persian)
Kratzert F, Klotz D, Brenner C, Schulz K, Herrnegger M (2018) Rainfall–runoff modelling using long short-term memory (LSTM) networks. Hydrology and Earth System Sciences 22(11):6005-6022
Kuok KK, and Bessaih N (2007) Artificial neural networks (ANNS) for daily rainfall runoff modelling. The Journal of the Institution of Engineers, Malaysia 68(3):31-42
Kuok KK, Harun S, and Shamsudin SM (2010) Global optimization methods for calibration and optimization of the hydrologic Tank model’s parameters. Canadian Journal on Civil Engineering 1(1):2-14
Lorrai M, and Sechi GM (1995) Neural nets for modelling rainfall-runoff transformations. Water Resources Management 9(4):299-313
Mirabedini S (2018) An overview of deep learning. 3rd National Technology Conference on Electrical and Computer Engineering (In Persian)
Moosavi V, Mahjoobi J, and Hayatzadeh M (2021) Combining group method of data handling with signal processing approaches to improve accuracy of groundwater level modeling. Natural Resources Research 30(2):1735-1754
Moosavi V, Talebi A, and Hadian MR (2017) Development of a hybrid wavelet packet-group method of data handling (WPGMDH) model for runoff forecasting. Water Resources Management 31(1):43-59
Moosavi V, Gheisoori Fard Z, Vafakhah M (2022) Which one is more important in daily runoff forecasting using data driven models: Input data, model type, preprocessing or data length?
Journal of Hydrology 606:127429
Patel AB, and Joshi GS (2017) Modeling of rainfall-runoff correlations using artificial neural network-A case study of Dharoi Watershed of a Sabarmati River Basin, India. Civil Engineering Journal 3(2):78-87
Razavizadeh S, and Dargahian F (2018) Optimizing the neural network structure in predicting sediment discharge using Taguchi method. Iranian Journal of Watershed Science and Engineering 12(43):89-97 (In Persian)
Riad S, Mania J, Bouchaou L, and Najjar Y (2004) Rainfall-runoff model using an artificial neural network approach. Mathematical and Computer Modelling 40(7-8):839-846
Rohani A, Saedi SE, Gerailu H, Aghkhani MH (2015) Prediction of lateral surface, volume and sphericity of pomegranate using MLP artificial neural network. Journal of Agricultural Machinery 5(2):292-301 (In Persian)
Schmidhuber J (2015) Deep learning in neural networks: An overview. Neural networks 61:85-117
Sedighi F, Vafakhah M, and Javadi M (2015) Application of artificial neural network in predicting runoff caused by snowmelt (case study: Letian dam watershed). Watershed Management Research 6(12):43-54 (In Persian)
Seo Y, Kim S, and Singh VP (2018) Machine learning models coupled with variational mode decomposition: A new approach for modeling daily rainfall-runoff. Atmosphere 9(7):251
Solomatine DP, and Ostfeld A (2008) Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics 10(1):3-22
Tikhamarine Y, Souag-Gamane D, Najah Ahmed A, Sammen SS, Kisi O, Feng Huang Y, El-Shafie A (2020) Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization.
Journal of Hydrology 589:125133
Xiang Z, Yan J, and Demir I (2020) A rainfall‐runoff model with LSTM‐based sequence‐to‐sequence learning. Water Resources Research 56(1):e2019WR025326
Zorati A, Salajegheh A, Almaali N, Mohammadaskari H (2009) Investigating the rainfall-runoff model using artificial neural network and statistical two-variable regression methods (Case Study: Minab watershed). Watershed Research (Research and Construction) 22(2):69-74 (In Persian)