Anctil F, Perrin C, and Andréassian V (2004) Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environmental Modelling and Software 19(4):357–368
Ghorbani MA, Azani A, and Vanolya SM (2015) Rainfall-runoff modeling using hybrid intelligent models. Journal of Iran-Water Resources Research 11(2):146-150 (In Persian)
Hu G, Wang K, Peng Y, Qiu M, Shi J, and Liu L (2018) Deep learning methods for underwater target feature extraction and recognition. Computational Intelligence and Neuroscience, doi: 10.1155/2018/1214301
Kim B, Jeong H, Kim H, and Han B (2017) Exploring wavelet applications in civil engineering. KSCE Journal of Civil Engineering 21(4):1076–1086
Liu Y, Zhang T, Kang A, Li J, and Lei X (2021) Research on runoff simulations using deep-learning methods. Sustainability (Switzerland) 13(3):1–20
Maheswaran R and Khosa R (2012) Comparative study of different wavelets for hydrologic forecasting. Computers and Geosciences 46:284–295
Mehrparvar M and Asghari K (2018) Modular optimized data assimilation and support vector machine for hydrologic modeling. Journal of Hydroinformatics 20(3):728–738
Mosavi A, Ozturk P, and Chau KW (2018) Flood prediction using machine learning models: Literature review. Water (Switzerland) 10(11):1–40
Nourani V (2017) An Emotional ANN (EANN) approach to modeling rainfall-runoff process. Journal of Hydrology, Elsevier B.V. 544:267–277
Nourani V and Komasi M (2013) A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process. Journal of Hydrology, Elsevier B.V. 490:41–55
Nourani V, Komasi M, and Mano A (2009) A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resources Management 23(14):2877–2894
Nourani V and Molajou A (2017) Application of a hybrid association rules/decision tree model for drought monitoring. Global and Planetary Change 159:37–45
Nourani V and Parhizkar M (2013) Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall-runoff modeling. Journal of Hydroinformatics 15(3):829–848
Partovian A, Nourani V, and Alami MT (2016) Hybrid denoising-jittering data processing approach to enhance sediment load prediction of muddy rivers. Journal of Mountain Science 13(12):2135–2146
Sang YF, Wang D, Wu JC, Zhu QP, and Wang L (2009) Entropy-based wavelet de-noising method for time series analysis. Entropy 11(4):1123–1147
Shafeizadeh M, Fathian H, and Nikbakht AR (2019) Continuous rainfall-runoff simulation by artificial neural networks based on selection of effective input variables using partial mutual information (PMI) algorithm. Journal of Iran-Water Resources Research 15(2):144-161 (In Persian)
Sharghi E, Nourani V, Molajou A, and Najafi H (2019) Conjunction of emotional ANN (EANN) and wavelet transform for rainfall-runoff modeling. Journal of Hydroinformatics 21(1):136–152
Shoaib M, Shamseldin AY, Khan S, Sultan M, Ahmad F, Sultan T, Dahri ZH, and Ali I (2019) Input selection of wavelet-coupled neural network models for rainfall-runoff modelling. Water Resources Management 33(3):955–973
Song CM (2022) Data construction methodology for convolution neural network based daily runoff prediction and assessment of its applicability. Journal of Hydrology, Elsevier B.V. 605:127324
Van SP, Le HM, Thanh DV, Dang TD, Loc HH, and Anh DT (2020) Deep learning convolutional neural network in rainfall-runoff modelling. Journal of Hydroinformatics 22(3):541–561
Wang W and Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nature and Science 1(1):67–71
Xu Y, Liu Yi, Jiang, Z and Yang, X (2021) Runoff prediction model based on improved convolutional neural network runoff prediction model based on improved convolutional neural network. Journal of Water Resources Management, 1-48, DOI: https://doi.org/10.21203/rs.3.rs-760130/v1.