Author(s): Thendiyath Roshni; Dar Himayoun; Farooq Mohsin

Linked Author(s):

Keywords: Rainfall-runoff process; Soft computing; Artificial neural networks; Multivariate adaptive regression splines; Jhelum basin; India; Peak events

Abstract: Accurate simulation of extreme events of runoff is very important for public safety and hydrological engineering. The simulation of extreme events mainly employs the rainfall-runoff models. This paper tests the potential of soft computing technique-based rainfall-runoff models to simulate the peak runoff events in order to develop an alarming system. This study mainly focusses on hybrid Artificial Neural Networks (ANNs) and Multivariate Adaptive Regression Splines (MARS). The six different regression models used are conventional Artificial Neural Network (ANN), Wavelet Artificial Neural Network (WANN), Bootstrapped Artificial Neural Network (BANN), Wavelet Bootstrapped Artificial Neural Network (WBANN), Multivariate Adaptive Regression Splines (MARS) and Wavelet Multivariate Adaptive Regression Splines (WMARS). The potential inputs were selected based on Auto Correlation Function (ACF) and Cross Correlation Functions (CCF). To implement the methodology, the Jhelum basin in the northern part of India was selected . Based on the results, it was found that all the models except BANN showed overall good performance. The WANN model (NSE = 0.95, RMSE = 1943.15 cusecs, MAPE = 25.5, R = 0.96, DA = 46.8) shows slightly better performance than ANN, WBANN, MARS and WMARS and far better than BANN. The accuracy of the forecasts was checked between WANN and ANN, WBANN and BANN, WMARS and MARS. The results show that decomposition method improves the forecasting accuracy of time series data. Again, the simulation of the peak events was done using the above six models. The efficiency of the models was evaluated on the basis of Normalized Root Mean Square Error (NRMSE). It was found that WANN outperforms the other five models with NRMSE (0.37) for all peak events. All the other models except BANN showed fair results with NRMSE for ANN = 0.66, MARS = 0.68, WBANN = 0.68 and WMARS = 0.53. Thus, it is recommended from the present study that WANN model is more promising for the simulation of peak events in time series data.

DOI: https://doi.org/10.1080/15715124.2019.1570934

Year: 2020