Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling

Rainfall-runoff (r-r) modeling at different time scales is considered as a significant issue in hydro-environmental planning. As a first hydrological implementation, for one-time-ahead r-r modeling of two watersheds with totally distinct climatic conditions, Genetic Algorithm (GA, as a global search technique) and Emotional Artificial Neural Network (EANN, as a new production of Artificial Intelligence (AI) based methods that simulated based on the brain neurophysiological structure) was combined. Determining the optimal architecture of AI-based networks is vital for increasing the accuracy of prediction by the network and also to reduce run-time. In the current study, GA has been implemented to choose the important features candidate as EANN input and automatically diagnose the optimal number of hidden nodes and hormones simultaneously. The acquired results indicated a better representation of the proposed hybrid GA-EANN model compared to the sole ANN and EANN. Numerical identification of obtained results revealed that the proposed hybrid GA-EANN model might enhance the better results than the EANN model up to 19% and 35% in terms of testing suitability criteria for Aji Chai and Murrumbidgee catchments, respectively.

Automated summary

Rainfall-runoff modeling at different time scales is a significant issue in hydro-environmental planning. In this study, a hybrid GA-EANN model was proposed and showed better performance compared to sole ANN and EANN models. The hybrid model achieved up to 19% and 35% improvement in testing suitability criteria for the Aji Chai and Murrumbidgee catchments, respectively.