Ensemble neural networks with input optimization for flood forecasting

Abstract

Machine learning model has been widely used to provide flood forecasting including the ensemble model. This paper proposed an ensemble of neural networks for long-term flood forecasting that combine the output of backpropagation neural network (BPNN) and extreme learning machine (ELM). The proposed ensemble neural networks model has been applied towards the rainfall data from eight rainfall stations of Kelantan River Basin to forecast the water level of Kuala Krai. The aim is to highlight the improvement on accuracy of the forecast. Prior to the development of such ensemble model, data are optimized in two steps which are decomposed it using discrete wavelet transform (DWT) to reduce variations in the rainfall series and selecting dominant features using entropy called mutual information (MI) for the model. The result of the experiments indicates that ensemble neural networks model based on the data decomposition and entropy feature selection has outperformed individually executed forecast model in term of RMSE, MSE and NSE. This study proved that the proposed method has reduce the data variance and provide better forecasting with minimal error. With minimal forecast error the generalization of the model is improved.