Abstract
The prediction of riverflows requires the understanding of rainfall-runoff process which is highly nonlinear, dynamic and complex in nature. In this research streamflow decomposition based integrated ANN (SD-ANN) model is developed to improve the efficacy rather than using a single ANN model for the flow hydrograph. The streamflows are decomposed into two states namely 1) the rise state and 2) the fall state. The rainfall-runoff data obtained from the Kolar River basin is used to test the efficacy of the proposed model when compared to feed-forward ANN model (FF-ANN). The results obtained in this study indicate that the proposed SD-ANN model outperforms the single ANN model in terms of both the statistical indices and the prediction of high flows.
Highlights
A wide variety of rainfall-runoff models have been developed and applied for water resources planning which is vital in terms of flood control and management
The results obtained in this study indicate that the proposed SD-Artificial neural network (ANN) model outperforms the single ANN model in terms of both the statistical indices and the prediction of high flows
The performance of the proposed SD-ANN model and FF-ANN model have been evaluated by means of a variety of statistical criteria such as coefficient of correlation (CC), coefficient of efficiency (NSE) and the root-mean-square error (RMSE) between the actual and estimated flow values
Summary
A wide variety of rainfall-runoff models have been developed and applied for water resources planning which is vital in terms of flood control and management. The hydrologists and water resources researchers have used conventional modeling techniques either deterministic models that includes physics of the underlying process or systems theoretic (black box) models These models require a large quantity of data and a complex methodology for its calibration. Studies include automated base flow separation and recession analysis [4], spectral analysis [5], wavelet transforms and runoff time series analysis [6,7,8,9], modular neural network (MNN) [10], self-organizing map (SOM) classifier [11,12] and self organizing linear output map (SOLO) [13] Most of these studies conclude that the decomposition and partitioning of data resulted in better model performance.
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