Hydrological Modeling Using Chicken Swarm Optimization Algorithm – Oued El Melah Case Study (NE of Algeria)
1
Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Amar Telidji University, P.O. Box 37.G, 03000 Laghouat, Algeria
2
Material and Energies Research Laboratory, Faculty of Science and Technology, Amine Elokkal El Hadj Moussa Egakhamouk University, Tamanrasset, Algeria
3
University of Science and Technology of Oran Mohamed Boudiaf, B.P. 1505, El M’Naouer Oran, Algeria
These authors had equal contribution to this work
Corresponding author
Oulad Naoui Noureddine
Material and Energies Research Laboratory, Faculty of Science and Technology, Amine Elokkal El Hadj Moussa Egakhamouk University, Tamanrasset, Algeria
Material and Energies Research Laboratory, Faculty of Science and Technology, Amine Elokkal El Hadj Moussa Egakhamouk University, Tamanrasset, Algeria
Ecol. Eng. Environ. Technol. 2024; 8:109-119
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ABSTRACT
The aim of this article is Hydrological modeling using Chicken Swarm Optimization Algorithm. Interactions between meteorological and hydrometric data been identified as a major conducting factor stationarity in rainfall-runoff relationships. The methodology used in this work is rainfall-runoff modeling is Chicken Swarm Optimization Algorithm. This approach based on randomly selection and chicken society behaivoir. For the efficiency evaluation of Chicken Swarm Optimization Algorithm, a variety of statistical parameters have been used with acceptable values of Nash–Sutclife Efciency (NSE) with 0.38% and Index of Agreement (IA) with 0.91% . The important and new results are the hydrological models (empirical models) adaptation to Algerian conditions especially in ungagged basins. Therefore, the proposal of the present modelling is a technique using the CSO method to model the rainfall-runoff relationship and discharge forecasting in the OUED EL MELAH basin located in GUELMA city. CSO proves to be a valuable model for studies of the rainfall and flood runoff response to protection of agglomerations against flooding risks projections.
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