This study presents the development of a rainfall-runoff model for the Bagmati River Basin using Artificial Neural Network (ANN) techniques. Recognizing the crucial role of accurate runoff estimation for flood forecasting, water resource planning, and environmental management, a three-layered feedforward ANN model with backpropagation was employed. The model was trained and validated using monthly and seasonal rainfall-runoff data from 2000 to 2009. Three different data set combinations were analyzed to assess model performance sensitivity with varying calibration and validation periods. Among them, the dataset calibrated with the entire 2000-2009 period and validated over 2007-2009 produced the most accurate results. Statistical performance metrics affirmed the ANN model's capability to capture the non-linear characteristics of the rainfall-runoff relationship effectively. This study highlights the robustness, adaptability, and predictive strength of ANN in hydrological modeling applications.
This study presents the development of a rainfall-runoff model for the Bagmati River Basin using Artificial Neural Network (ANN) techniques. Recognizing the crucial role of accurate runoff estimation for flood forecasting, water resource planning, and environmental management, a three-layered feedforward ANN model with backpropagation was employed. The model was trained and validated using monthly and seasonal rainfall-runoff data from 2000 to 2009. Three different data set combinations were analyzed to assess model performance sensitivity with varying calibration and validation periods. Among them, the dataset calibrated with the entire 2000-2009 period and validated over 2007-2009 produced the most accurate results. Statistical performance metrics affirmed the ANN model's capability to capture the non-linear characteristics of the rainfall-runoff relationship effectively. This study highlights the robustness, adaptability, and predictive strength of ANN in hydrological modeling applications.
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