Efficacy in simulating the peak discharge response using soft computing techniques in the Jhelum river basin, India

References

• Adamowski, Jan, and Sun, Karen, 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 (1–2), 85–91.
   Web of Science ®Google Scholar
• Bates, Bryson C., and Townley, Lloyd R., 1988. Nonlinear discrete flood event models 3- analysis of prediction uncertainty. Journal of Hydrology, 99 (1–2), 91–101.
   Web of Science ®Google Scholar
• Chandwani, V., et al., 2015. Soft computing approach for rainfall-runoff modelling. Aquatic Procedia, 4 (2), 1054–1061.
   Google Scholar
• Cheng, C.T., Ou, C.P., and Chau, K.W., 2002. Combining a Fuzzy optimal model with a Genetic Algorithm to solve multi-objective rainfall–runoff model calibration. Journal of Hydrology, 268 (1–4), 72–86.
   Web of Science ®Google Scholar
• Dawson, C.W., Abrahart, R.J., and See, L.M., 2007. Hydrotest: a web-based Toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling and Software, 22 (7), 1034–1052.
   Web of Science ®Google Scholar
• Demirel, M.C., Anabela, V., and Ercan, K., 2009. Flow forecast by SWAT model and ANN in Pracana basin, Portugal. Advances in Engineering Software, 40 (7), 467–473.
   Web of Science ®Google Scholar
• Deo, R.C., Ozgur, K., and Vijay, P.S., 2017. Drought forecasting in Eastern Australia using Multivariate Adaptive regression spline, least square Support vector Machine and M5Tree model. Atmospheric Research, 184 (1-7), 149–175.
   Google Scholar
• Du, K., Zhao, Y., and Lei, J., 2017. The incorrect usage of singular spectral analysis and discrete wavelet transform in hybrid models to predict hydrological time series. Journal of Hydrology, 552 (06), 44–51.
   Google Scholar
• Efron, B., 1979. Bootstrap methods: another look at the Jackknife. The Annals of Statistics, 7 (1), 1–26.
   Web of Science ®Google Scholar
• Friedman, 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19 (11), 1–6.
   Google Scholar
• Ghumman, A.R., et al., 2011. Runoff forecasting by artificial neural network and conventional model. Alexandria Engineering Journal, 50 (4), 345–350.
   Google Scholar
• Jain, A., and Srinivasulu, Sanaga, 2004. Development of effective and efficient rainfall-runoff models using integration of deterministic, real-coded genetic algorithms and artificial neural network techniques. Water Resources Research, 40 (4), 1–12.
   Web of Science ®Google Scholar
• Kisi, O., 2015. Streamflow forecasting and estimation using least square support vector regression and adaptive neuro-fuzzy embedded fuzzy c-means clustering. Water Resource Management, 29 (8), 5109–5127.
   Google Scholar
• Maier, H.R., and Dandy, Graeme C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software, 15 (1), 101–124.
   Web of Science ®Google Scholar
• Mallat, S.G., 1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11 (7), 674–693.
   Web of Science ®Google Scholar
• Minns, A.W., and Hall, M. J., 1996. Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal, 41 (3), 399–417.
   Web of Science ®Google Scholar
• Moosavi, V, Talebi, A, and Hadian, Mohammad R, 2017. Development of a hybrid wavelet packet- group method of data handling (WPGMDH) model for runoff forecasting. Water Resources Management, 31 (9), 43–59.
   Google Scholar
• Mundher, Y.Z., et al., 2016. Boost stream flow forecasting model with extreme learning machine, data driven: a case study in semi-arid region in Iran. Journal of Hydrology, 542 (4), 603–614.
   Google Scholar
• Quilty, J., and Adamowski, J., 2018. Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. Journal of Hydrology, 353 (5), 247–266.
   Google Scholar
• Nourani, Vahid, Komasi, Mehdi, and Mano, Akira, 2009. A multivariate ANN-wavelet approach for rainfall-runoff Modeling. Water Resources Management, 23 (1-4), 2877–2894.
   Google Scholar
• Rezaie-balf, Mohammad, et al., 2017. Wavelet coupled MARS and M5 model tree approaches for groundwater level forecasting. Journal of Hydrology, 553 (1-8), 356–373.
   Google Scholar
• Rezaie-Balf, Mohammad, and Zahmatkesh, Zahra, 2017. Soft computing techniques for rainfall-runoff simulation : Local Non – Parametric Paradigm vs. model Classification Methods. Water Resources Management, 31 (6), 3843–3865.
   Google Scholar
• Rosso, O.A., et al., 2004. Analysis of wavelet-filtered Tonic-Clonic electroencephalogram recordings. Medical and Biological Engineering and Computing, 42 (4), 516–523.
   PubMed Web of Science ®Google Scholar
• Seo, Y., et al., 2014. Daily water level forecasting using wavelet decomposition and artificial intelligence techniques. Journal of Hydrology, 50 (11), 971–978.
   Google Scholar
• Sharda, V.N., et al., 2008. Performance of multivariate adaptive regression splines (MARS) in predicting runoff in mid-Himalayan micro-watersheds with limited data. Hydrological Sciences Journal, 53 (6), 1165–1175.
   Web of Science ®Google Scholar
• Sharma, A., and Goyal, M.K., 2016. A comparison of three soft computing techniques, Bayesian regression, support vector regression, and wavelet regression, for monthly rainfall forecast. J. Intell. Syst, 65 (4), 1–15.
   Google Scholar
• Shoaib, Muhammad, et al., 2018. A wavelet based approach for combining the outputs of different rainfall – runoff models. Stochastic Environmental Research and Risk Assessment, 32 (1), 155–168.
   Web of Science ®Google Scholar
• Singh, A., et al., 2018. Rainfall-runoff modeling in hilly watershed using heuristic approaches with Gamma test. Water Resources Management, 29 (1-4), 2877–2894.
   Google Scholar
• Solomatine, Dimitri P., and Dulal, Khada N., 2003. Model trees as an alternative to neural networks in rainfall-runoff modelling. Hydrological Sciences Journal, 48 (3), 399–411.
   Web of Science ®Google Scholar
• Sotomayor, K., and Latínez, A., 2010. Comparison of adaptive methods using multivariate regression splines (MARS) and artificial neural networks backpropagation (ANNB) for the forecast of Rain and Temperatures in the Mantaro river basin. Journal of Hydrology, 98 (4), 58–68.
   Google Scholar
• Tiwari, Mukesh K., and Chatterjee, Chandranath, 2011. A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics, 13 (3), 500.
   Web of Science ®Google Scholar
• Twomey, J.M., and Smith, A.E., 1998. Bias and variance of validation methods for function approximation neural networks under conditions of sparse data. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 28 (3), 417–430.
   PubMed Web of Science ®Google Scholar
• Wang, W, and Ding, J., 2003. Wavelet network model and its application to the prediction of hydrology. Nature and Science, 1 (1), 67–71.
   Google Scholar
• Wu, C.L., and Chau, K.W., 2011. Rainfall – runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology, 554 (1-5), 503–515.
   Google Scholar
• Yaseen, Z.M., et al., 2017. Novel approach for Streamflow forecasting using a hybrid ANFIS- FFA model. Journal of Hydrology, 542 (9), 603–614.
   Google Scholar