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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 are 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, 12 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 m3/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. [ABSTRACT FROM AUTHOR]

The sedimentation behaviour of Getalsud Reservoir in the Jharkhand State of India was studied, and its useful life and performance forecasted. The quantity of sediment and its vertical distribution over different reservoir elevations have been estimated using standard methods. The study results indicate the provisions made for sediment deposit in the reservoir are sufficient, and the useful life of the reservoir will be longer than its design life of 100 years. The trap-efficiency of the reservoir is not anticipated to vary much until the year 2250, after which it is expected to rapidly decrease. In the first 100 years of impoundment, the reservoir loses 60% of its dead storage, 13% of its live storage and 24% of its gross storage. At its present sedimentation rate, Getalsud Reservoir may become fully obsolete by the year 2500. [ABSTRACT FROM AUTHOR]

A new expression for the prediction of longitudinal dispersion coefficient in natural rivers, using genetic algorithms, is proposed. The expression uses hydraulic and geometric characteristics of rivers, which are readily available. For performance evaluation, using published field data, results of coefficient prediction by the new expression and by the other reported expressions are compared. According to various performance indices, it is concluded that the new formula predicts the longitudinal dispersion coefficient more accurately. Sensitive analysis performed on input parameters indicates the ratio of the cross-sectional mean velocity to the bottom shear velocity to be the most influencing parameter for accurate prediction of the longitudinal dispersion coefficient. [ABSTRACT FROM AUTHOR]