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

• Published in: Water resources management Vol. 35; no. 8; pp. 2369 - 2384
• Main Authors: Molajou, Amir, Nourani, Vahid, Afshar, Abbas, Khosravi, Mina, Brysiewicz, Adam
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Atmospheric Sciences; brain; Catchments; Civil Engineering; Climatic conditions; Earth and Environmental Science; Earth Sciences; Emotions; Environment; Environmental planning; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hormones; Hydrogeology; Hydrologic models; Hydrology; Hydrology/Water Resources; Modelling; Neural networks; neurophysiology; prediction; Rain; Rainfall; Rainfall-runoff relationships; Runoff; Watersheds; Genetic algorithm (GA); Feature selection; Structure optimization; Rainfall-runoff modeling; Emotional artificial neural network (EANN)
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-021-02818-2

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.