African Journal of
Environmental Science and Technology

  • Abbreviation: Afr. J. Environ. Sci. Technol.
  • Language: English
  • ISSN: 1996-0786
  • DOI: 10.5897/AJEST
  • Start Year: 2007
  • Published Articles: 1055

Full Length Research Paper

Integration of hydro-climatic data and land use in neural networks for modeling river flows: Case of Lobo river in the southwest of Cote d’ivoire

Yao Blaise KOFFI1*, Kouassi Ernest AHOUSSI1, Amani Michel KOUASSI2, Ouattara KOUASSI1, Loukou Christophe KPANGUI1 and Jean BIEMI1
  1Université de Cocody, Unité de Formation et de Recherche (UFR) des Sciences de la Terre et des Ressources Minières (STRM), 22 BP 582 Abidjan 22, Abidjan, Côte d’Ivoire. 2Institut National Polytechnique Félix Houphouët-Boigny (INP-HB), Département des Sciences de la Terre et des Ressources Minières (STeRMi), BP 1093 Yamoussoukro, Côte d’Ivoire
Email: [email protected]

  •  Accepted: 30 August 2013
  •  Published: 31 August 2013

Abstract

 

In a context of climate disruption due to uncontrolled human activities, the classical models of rainfall-runoff modeling are almost unusable. In addition on the Lobo River (Southwest of Côte d’Ivoire), no simulation study has been carried out yet despite that, this river has flooded fields and villages causing huge losses in September 2007. Neural networks appear in this case as a solution for simulating flows in the context of non-linearity between rainfall and flow of this river. Climatic data (rainfall, temperature and PET) and land use will be phased in neural models to simulate monthly flows of the river Lobo. Four (4) neuronal model variants were constructed from three (3) hydro-climatic parameters (rainfall, potential evapotranspiration and flow) and the land acquired from Landsat ETM + 1990 and ETM + 2000. Two types of models have been created: the unguided model and the guided model. The simulation with the unguided model did not provide a satisfactory result. In effect, the value of Nash is only 22.90%. However, the NASH value of the guided model is much better than the previous one (85.01, 83.38 and 84.05%). These results help to highlight the importance of land use on the performance of neural networks. This study also demonstrated the ability of artificial neural networks to simulate the nongauged river flows in the context of climate disruption.

 

Key words: Côte d’Ivoire, Lobo, multilayer perception, remote sensing, simulation, hydrology, model, neuron,  flow