patrick namu (5 risultati)

Paperback. Condizione: Brand New. 124 pages. 8.66x5.91x0.28 inches. In Stock.

Taschenbuch. Condizione: Neu. Optimizing Hydraulic Design parameters for Irrigation Settling Basin | Predicting settlement for discrete particles in settling basins for irrigation systems | Patrick Namu (u. a.) | Taschenbuch | 124 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786133999541 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The management of sediments in river basins and waterways has been an important issue for water managers throughout history. Water Engineers and managers are faced with similar challenges caused by siltation of water reservoirs and irrigation water conveyance systems. As a copping strategy to counter the low irrigation application efficiency, designs of settling tanks are typically oversized. This is aimed at having enough detention time for the sediment particles to settle. This process is costly and tedious hence this book is aimed at optimizing the hydraulic design parameters for settling basins with an intention of addressing such challenges. Artificial Neural Networks in MATLAB was used to build ANN models for predicting sediment settlement and eventually optimizing the hydraulic parameters of settling basins. The ANN was used to develop the relation between sediment settlement and different inflows by changing the different number of neurons in hidden layer. The developed simulated results were evaluated using recommended quantitative statistical analysis that compared the measured and the predicted data. 124 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The management of sediments in river basins and waterways has been an important issue for water managers throughout history. Water Engineers and managers are faced with similar challenges caused by siltation of water reservoirs and irrigation water conveyance systems. As a copping strategy to counter the low irrigation application efficiency, designs of settling tanks are typically oversized. This is aimed at having enough detention time for the sediment particles to settle. This process is costly and tedious hence this book is aimed at optimizing the hydraulic design parameters for settling basins with an intention of addressing such challenges. Artificial Neural Networks in MATLAB was used to build ANN models for predicting sediment settlement and eventually optimizing the hydraulic parameters of settling basins. The ANN was used to develop the relation between sediment settlement and different inflows by changing the different number of neurons in hidden layer. The developed simulated results were evaluated using recommended quantitative statistical analysis that compared the measured and the predicted data.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The management of sediments in river basins and waterways has been an important issue for water managers throughout history. Water Engineers and managers are faced with similar challenges caused by siltation of water reservoirs and irrigation water conveyance systems. As a copping strategy to counter the low irrigation application efficiency, designs of settling tanks are typically oversized. This is aimed at having enough detention time for the sediment particles to settle. This process is costly and tedious hence this book is aimed at optimizing the hydraulic design parameters for settling basins with an intention of addressing such challenges. Artificial Neural Networks in MATLAB was used to build ANN models for predicting sediment settlement and eventually optimizing the hydraulic parameters of settling basins. The ANN was used to develop the relation between sediment settlement and different inflows by changing the different number of neurons in hidden layer. The developed simulated results were evaluated using recommended quantitative statistical analysis that compared the measured and the predicted data.