9789999328609 - Rainfall-Runoff Modelling of Bagmati River Basin Using Ann Technique: Soft Computing Techniques de Kumar, Keshav (11 resultados)

Paperback. Condición: Brand New. 77 pages. 6.00x0.18x9.00 inches. In Stock.

Paperback. Condición: new. Paperback. 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 item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Print on Demand.

Condición: New. Print on Demand.

Condición: New. PRINT ON DEMAND.

Paperback. Condición: new. Paperback. 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 item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condición: new. Paperback. 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 item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Taschenbuch. Condición: Neu. Rainfall-Runoff Modelling of Bagmati River Basin Using Ann Technique | Keshav Kumar | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999328609 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.