摘要
Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies difficult. Generally, conceptual rain-flow models are designed to bring an appropriate answer with the correction of gaps and prediction of the flows. Historical hydro-climatic data of the Ity station, located on Cavally River, contain gaps which must be bridged. This study aims to establish a rainfall-runoff model through artificial neural networks for filling the gaps into the flow data series of the hydrometric station of Ity on the watershed of Cavally River. A multi-layer perceptron of feed forwards with two entries (monthly average rain and evapotranspiration) and an exit (flows) was established with flow evapotranspiration data. Comparison of the criteria of performance of the various architectures of the neural network model showed that architecture 2-3-1 gives best results. This architecture provides Nash coefficients of 75.79% and correlation linear coefficient of 95.64% for the calibration and Nash coefficients of 73.32% and correlation linear coefficient of 98.33% for the validation. The correlations between simulated flows and observed flows are strong. The correlation coefficients are 83.89% and 83.08% respectively for the calibration and validation.
Water resources management is nowadays a significant stake for the world. However, missing or bad quality of the hydro-climatic historical data available of the studied area makes sometimes hydrological studies difficult. Generally, conceptual rain-flow models are designed to bring an appropriate answer with the correction of gaps and prediction of the flows. Historical hydro-climatic data of the Ity station, located on Cavally River, contain gaps which must be bridged. This study aims to establish a rainfall-runoff model through artificial neural networks for filling the gaps into the flow data series of the hydrometric station of Ity on the watershed of Cavally River. A multi-layer perceptron of feed forwards with two entries (monthly average rain and evapotranspiration) and an exit (flows) was established with flow evapotranspiration data. Comparison of the criteria of performance of the various architectures of the neural network model showed that architecture 2-3-1 gives best results. This architecture provides Nash coefficients of 75.79% and correlation linear coefficient of 95.64% for the calibration and Nash coefficients of 73.32% and correlation linear coefficient of 98.33% for the validation. The correlations between simulated flows and observed flows are strong. The correlation coefficients are 83.89% and 83.08% respectively for the calibration and validation.
