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Two hybrid AI-based models which are reliable in capturing the periodicity features of the process are introduced for watershed rainfall–runoff modeling and show that the second model is relatively more appropriate because it uses the multi-scale time series of rainfall and runoff data in the ANFIS input layer. Expand
Summary The study investigates the accuracy of wavelet and support vector machine conjunction model in monthly streamflow forecasting. The conjunction method is obtained by combining two methods,… Expand
A new conjunction method (wavelet-neuro-fuzzy) for precipitation forecast is proposed in this study. The conjunction method combines two methods, discrete wavelet transform and neuro-fuzzy. The… Expand
Summary Pan evaporation (Ep) modeling is an important issue in reservoir management, regional water resources planning and evaluation of drinking-water supplies. The main purpose of this study is to… Expand
TLDRFour different ANN algorithms, namely, backpropagation, conjugate gradient, cascade correlation, and Levenberg–Marquardt are applied to continuous streamflow data of the North Platte River in the United States and the results are compared with each other.Expand
Summary Wavelet regression (WR) technique is proposed for short-term streamflow forecasting in this study. The WR model is improved combining two methods, discrete wavelet transform and linear… Expand
Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently… Expand
Summary This study proposes genetic programming (GP) as a new approach for the explicit formulation of daily suspended sediment–discharge relationship. Empirical relations such as sediment rating… Expand
The neuro-wavelet model is improved by combining two methods, discrete wavelet transform and multi-layer perceptron, for one-month-ahead stream flow forecasting and results revealed that the suggested model could increase the forecast accuracy and perform better than the MLP, MLR and AR models. Expand
The present review focuses on defining hybrid modeling, the advantages of such combined models, as well as the history and potential future of their application in hydrology to predict important processes of the hydrologic cycle. Expand
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