A Comparison of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) Approach for Rainfall-Runoff Modelling

Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set ۱, Set ۲ and Set ۳ have been prepared using weather and discharge data of ۲ raingauge stations and ۱ discharge station located in the command for the period ۱۹۸۶-۲۰۱۴. Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies. The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set ۲ showed that ENN with R= ۰.۹۳۳, R۲ = ۰.۸۷۰, Nash Sutcliffe = ۰.۸۶۸۹, RMSE = ۲۷۶.۱۳۵۹ and Relative Peak Error = ۰.۰۰۸۷۹ outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.Reliable method of rainfall-runoff modeling is a prerequisite for proper management and mitigation of extreme events such as floods. The objective of this paper is to contrasts the hydrological execution of Emotional Neural Network (ENN) and Artificial Neural Network (ANN) for modelling rainfall-runoff in the Sone Command, Bihar as this area experiences flood due to heavy rainfall. ENN is a modified version of ANN as it includes neural parameters which enhance the network learning process. Selection of inputs is a crucial task for rainfall-runoff model. This paper utilizes cross correlation analysis for the selection of potential predictors. Three sets of input data: Set ۱, Set ۲ and Set ۳ have been prepared using weather and discharge data of ۲ raingauge stations and ۱ discharge station located in the command for the period ۱۹۸۶-۲۰۱۴. Principal Component Analysis (PCA) has then been performed on the selected data sets for selection of data sets showing principal tendencies. The data sets obtained after PCA have then been used in the model development of ENN and ANN models. Performance indices were performed for the developed model for three data sets. The results obtained from Set ۲ showed that ENN with R= ۰.۹۳۳, R۲ = ۰.۸۷۰, Nash Sutcliffe = ۰.۸۶۸۹, RMSE = ۲۷۶.۱۳۵۹ and Relative Peak Error = ۰.۰۰۸۷۹ outperforms ANN in simulating the discharge. Therefore, ENN model is suggested as a better model for rainfall-runoff discharge in the Sone command, Bihar.