Wavelet-genetic programming conjunction model for flood forecasting in rivers

In this study we have developed a conjunction model, WGP, of discrete wavelet transform (DWT) and genetic programming (GP) for forecasting river floods when the only data available is the historical daily flows. DWT is used for denoising and smoothening the observed flow time series on which GP is implemented to get the next-day flood. The new model is compared with autoregressive (AR) and stand-alone GP models. All models are calibrated and tested on the Kosi River which is one of the most devastating rivers of the world with high and spiky monsoon flows, modeling of which poses a great challenge. With different inputs, twelve models, four in each class of WGP, GP and AR are devised. The best performing WGP model, WGP4, with four previous daily flow rates as input, forecasts the Kosi floods with an accuracy of 87.9%, root mean square error of 123.9 m 3 /s and Nash–Sutcliffe coefficient of 0.993, the best performance indices among all the developed models. The extreme floods are also better simulated by the WGP models than by AR and GP models.