- Abda, Z., Chettih, M., & Zerouali, B. (2021). Assessment of neuro-fuzzy approach based diferent wavelet families for daily fow rates forecasting. Journal of Model Earth Syst Environent, 7, 1523–1538. https://doi.org/ 1007/s40808-020-00855-1
- Adamowski, J.F., & Sun, K. (2010). Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390(1), 85-91. https:// doi.org/1016/j.jhydrol.2010.06.033
- Ahmadi, F., & Maddah, M.A. (2020). Development of wavelet-Kstar algorithm hybrid model for the monthly precipitation prediction (case study: synoptic station of shvaz). Journal of Soil and Water Research, 52(2), 409-420. (In Persian). https://doi.org/22059/IJSWR.2021.314110.668808
- Ahmadi, M., Moeini, A., Ahmadi, H., Motamedvaziri, B., & Zehtabiyan, G.R. (2019). Comparison of the performance of SWAT, IHACRES and artifcial neural networks models in rainfall-runof simulation (case study: Kan watershed, Iran). Journal of Physics and Chemistry of the Earth, 111, 65–77. https://doi.org/ 1016/j.pce.2019. 05.002
- Ahooghalandari, M., Khiadani, M., & Kothapalli, G. (2016). Assessment of Artifcial Neural Networks and IHACRES models for simulating streamflow in Marillana catchment in the Pilbara, Western Australia. Austr Journal Water Resource, 19, 116–126.
- Araghinejad, S. (2014). Data-driven modeling: using MATLAB in water resources and environmental engineering. Journal of Water Science and Technology Library, 67, 265.
- Belayneh, A., & Adamowski, J. (2012). Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression. Journal of Applied Computational Intelligence and Soft Computing, 1–13. https://doi.org/10.1155/2012/794061
- Cannas, B., Alessandra, F., See, L., & Sias, G. (2006). Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Physics and Chemistry of the Earth, 31(18), 1164–1171. https://doi.org/1016/j.pce.2006.03.020
- Chakraborty, S., & Biswas, S. (2023). River discharge prediction using wavelet-based artificial neural network and long short-term memory models: a case study of Teesta River Basin, India. Stochastic Environmental Research and Risk Assessment, 37(8), 1-22. https://doi.org/1007/s00477-023-02443-y
- Chen, C.H. (1999). Wevelet approach to optimizing dynamic system. Control Theory and Applications. IEE Proceedings, 146(2), 213-219. https://doi.org/1049/ip-cta:19990516
- Danandeh Mehr, A., Kahya, E., & Olyaie, E. (2013). Streamfow prediction using linear genetic programming in comparison with a neurowavelet technique. Journal of Hydrology, 505, 240–249. https://doi.org/1016/j.jhydrol. 2013.10.003
- Dastorani, M.T., Hajibigloo, M., & Shojaee, H. (2022). Identification of the land use changes on river flooding bed, affective on reservoir water quality (Case study: headwater of Kardeh reservoir). Geography and Development, 20(66), 255-282. https://doi.org/22111/J10.22111.2022.6739
- Dalkiliç, H.Y., & Hashimi, S.A. (2020). Prediction of daily streamfow using artificial neural networks (ANNs), wavelet neural networks (WNNs), and adaptive neuro-fuzzy inference system (ANFIS) models. Water Supply, 20(4), 1396–1408. https://doi.org/2166/ws.2020.062
- Freire, P.K.d.M.M., Santos, C.A.G., & da Silva, G.B.L. (2019). Analysis of the use of discrete wavelet transforms coupled with ANN for shortterm streamflow forecasting. Apply Soft Computer, 80, 494–505. https://doi.org/1016/ j.asoc.2019.04.024
- Graps A. (1995). An Instroduction to wavelet. Computing in Science and Engineering, 2, 50-61. https://doi.org/ 1109/99.388960
- Grossmann, A., & Morlet, J. (1984). Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM Journal on Mathematical Analysis, 15(4), 723–736. https://doi.org/1137/0515056
- Güneş, M., Parim, C., Yıldız, D., & Büyüklü, A. (2021). Predicting monthly streamflow using a hybrid wavelet neural network: case study of the Çoruh River Basin. Polish Journal of Environmental Studies, 30(4), 3065–3075. https://doi.org/15244/pjoes/130767
- Jimeno-Saez, P., Senent-Aparicio, J., Perez-Sanchez, J., & PulidoVelazquez, D. (2018). A comparison of SWAT and ANN models for daily runof simulation in diferent climatic zones of peninsular Spain. Water, 10(2), 192. https://doi.org/3390/w10020192
- Katipoglu, O.M. (2023). Monthly streamfow prediction in Amasya, Türkiye, using an integrated approach of a feedforward backpropagation neural network and discrete wavelet transform. Modeling Earth Systems and Environment, 9(2), 1-13. https://doi.org/1007/s40808-022-01629-7
- Kim, T.W., Valdes, J.B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319–328. https://doi.org/1061/(ASCE) 1084-0699(2003)8:6(319)
- Maheswaran, R., Khosa, R. (2012). Comparative study of different wavelets for hydrologic forecasting. Computers and Geosciences, 46, 284–295. https://doi.org/1016/j.cageo.2011.12.015
- Mallat, S. (1999). A Wavelet Tour of Signal Processing, 2nd ed. Academic, New York.
- Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J.M. (2001). Wvelet toolbox for use with Matlab.
- Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2017). A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and k-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resources Management, 32(5), 243-258. https://doi.org/1007/s11269-017-1807-2
- Momeneh, S., & Nourani, V. (2022). Application of a novel technique of the multi‑discrete wavelet transforms in hybrid with artifcial neural network to forecast the daily and monthly streamfow. Modeling Earth Systems and Environment, 8(2), 4629–4648. https://doi.org/1007/s40808-022-01387-6
- Moriasi, D.N., Arnold, J.G., & Van Liew, M.W. (2007). Model evaluation guidelines for systemic quantification of accuracy in watershed simulations. Transactions of ASABE, 50(3), 885-900. https://doi.org/13031/2013.23153
- Nayak, P., Venkatesh, B., Krishna, B., & Jain, S.K. (2013). Rainfall-runof modeling using conceptual, data driven, and wavelet based computing approach. Journal of Hydrology, 493, 57–67. https://doi.org/1016/j.jhydrol. 2013.04.016
- Okkan, U. (2013). Wavelet neural network model for reservoir inflow prediction. Scientia Iranica, 19, 1445-1455. https://doi.org/10.1016/j.scient.2012.10.009
- Partal, T., Kisi, O. (2007). Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology 342(1):199–212. https://doi.org/1016/j.jhydrol.2007.05.026
- Poonia, V., & Tiwari, H.L. (2020). Rainfall-runoff modeling for the Hoshangabad Basin of Narmada River using artificial neural network. Arabian Journal of Geosciences, 13(18), 1–10. https://doi.org/1007/s12517-020-05930-6
- Rajendra, P., Murthy, K.V.N., Subbarao, A., & Boadh, R. (2019). Use of ANN models in the prediction of meteorological data. Modeling Earth Systems and Environment, 5(14), 1051–1058. https://doi.org/1007/s40808-019-00590-2
- Santos, C.A.G., & Silva, G.B.L. (2014). Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrological Sciences Journal 59(2) :312–324. https://doi.org/1080/02626667.2013.800944
- Santos, C.A.G., Freire, P.K.M.M., Silva, G.B.L., & Silva, R.M. (2014). Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir. Proceedings of the International Association of Hydrological Sciences, 364, 100–105. https://doi.org/10.5194/piahs-364-100-2014
- Shoaib, M., Shamseldin, A.Y., & Melville, B.W. (2014). Comparative study of different wavelet based neural network models for rainfall-runoff modelling. Journal of Hydrology, 515(1-2), 47-58. https://doi.org/1016/ j.jhydrol.2014.04.055
- Siddiqi, T.A., Ashraf, S., Khan, S.A., & Iqbal, M.J. (2021). Estimation of datadriven streamfow predicting models using machine learning methods. Arabian Journal of Geosciences, 14(11), 1058-1567. https://doi.org/1007/ s12517-021-07446-z
- Sithara, S., Pramada, S.K., & Thampi, S.G. (2020). Sea level prediction using climatic variables: a comparative study of SVM and hybrid wavelet SVM approaches. Acta Geophysica, 68, 1779–1790. https://doi.org/1007/s11600-020-00484-3
- Sun, Y., Niu, J., & Sivakumar, B. (2019). A comparative study of models for short-term streamflow forecasting with emphasis on waveletbased approach. Stochastic Environmental Research and Risk Assessment, 33, 1875–1891. https://doi.org/1007/s00477-019-01734-7
- Tareke, K.A., & Awoke, A.G. (2023). Hydrological drought forecasting and monitoring system development using artificial neural network (ANN) in Ethiopia. Heliyon, 9(2). https://doi.org/1016/j.heliyon.2023.e13287
- Tayyab, M., Zhou, J., Dong, X., Ahmad, I., & Sun, N. (2019). Rainfall-runof modeling at Jinsha River basin by integrated neural network with discrete wavelet transform. Meteorology Atmospheric Physics, 131(1), 115–125. https://doi.org/1007/s00703-017-0546-5
- Tiwari, D.K., Tiwari, H.L., & Nateriya, R. (2022). Runoff modeling in Kolar river basin using hybrid approach of wavelet with artificial neural network. Journal of Water and Climate Change, 13(3), 963. https://doi.org/2166/ wcc.2021.246
- Tiwari, M.K., & Chatterjee, C. (2011). A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics, 13(3), 500–519. https://doi.org/2166/hydro.2010.142
- Wagena, M.B., Goering, D., Collick, A.S., Bock, E., Fuka, D.R., Buda, A., & Easton, Z.M. (2020). Comparison of short-term streamfow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. Environmental Modelling & Software, 126(4), 104669. https://doi.org/1016/j.envsoft.2020.104669
- Wambua, R.M. (2014). Drought forecasting using indices and artificial neural networks for upper tana River Basin, Kenya-A review concept. Journal of Civil & Environmental Engineering, 4(4), 1-12. https://doi.org/4172/2165-784X.1000152
- Wang, W., & Ding, J. (2003). Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1(1), 67-71.
- Yilmaz, M., Tosunoğlu, F., Kaplan, N.H., Üneş, F., & Hanay, Y.S. (2022). Predicting monthly streamfow using artifcial neural networks and wavelet neural networks models. Modeling Earth Systems and Environment, 8(4), 3-20. https://doi.org/1007/s40808-022-01403-9
- Young, C.C., Liu, W.C., & Wu, M.C. (2017). A physically based and machine learning hybrid approach for accurate rainfallmodeling during extreme typhoon events. Applied Soft Computing, 53(3-4), 205–216. https://doi.org/1016/ j.asoc.2016.12.052
|