International Journal of Environmental Sciences

Volume 2 Issue 4 2012           Pages:2063- 2075

Modeling of annual flows using a conceptual model and an artificial neural network model in the N’zi-Bandama watershed (Côte d’Ivoire)

Author Information:

Amani Michel KOUASSI  Institut National Polytechnique Félix Houphouët-Boigny (INP-HB), Department of Earth Sciences and Mineral Resources (STeRMi), PoBox 1093 Yamoussoukro (Côte d’Ivoire)

Yao Blaise KOFFI University of Cocody-Abidjan, Laboratory of Science and Technology of Water and Environment (LSTEE), Department of  Earth Sciences and Mineral Resources (STRM), 22 PoBox 582 Abidjan 22 (Côte d’Ivoire)

Koffi Fernand KOUAME University of Cocody-Abidjan, Laboratory of Science and Technology of Water and Environment (LSTEE), Department of  Earth Sciences and Mineral Resources (STRM), 22 PoBox 582 Abidjan 22 (Côte d’Ivoire)

Théophile LASM University of Cocody-Abidjan, Laboratory of Science and Technology of Water and Environment (LSTEE), Department of  Earth Sciences and Mineral Resources (STRM), 22 PoBox 582 Abidjan 22 (Côte d’Ivoire)

Jean BIEMI University of Cocody-Abidjan, Laboratory of Science and Technology of Water and Environment (LSTEE), Department of  Earth Sciences and Mineral Resources (STRM), 22 PoBox 582 Abidjan 22 (Côte d’Ivoire)

ABSTRACT

This study presents a comparison between two models of the rainfall-runoff transformation on an annual basis: a conceptual model and an artificial neural network (ANN). Both models are applied to three watersheds of the N’zi River (Bandama) in Côte d’Ivoire. The comparative analysis is based on the performances of simulation in terms of Nash-Sutcliffe criterion. The models have been tested on two periods, a dry (1973-1997) and a wet one (1961-1972). The input data of the two models are the rain and the potential evapotranspiration to annual time step. The main results of this work show that the performances of both models (conceptual and neuronal) remain satisfactory in general with Nash-Sutcliffe criterion higher than 60%. These models appeared also robust and suitable for the simulation of the annual flows of rivers. The comparison of the two models has showed that the neural network performed significantly better than the conceptual model.

Key words: Rainfall-runoff modeling, conceptual model, artificial neural network, N’zi-Bandama, Côte d’Ivoire

DOI: 10.6088/ijes.00202030090


© 2012 Copyright Amani Michel KOUASSI et al, licensee IPA.This is an open access article distributed under the Creative Commons Attribution License (2.0) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The electronic version of the article can be downloaded below.

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