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Water is vital for life and the initial step in its management is to quantify the runoff produced in the catchment area due to rainfall. For stream flow measurement gauging of catchment is done. Because of the high cost involved in the setting up and maintenance of gauging stations, it is not possible to set up and maintain the stations over many locations for a long period of time. Thus, although many large catchments are gauged, a lot of small catchments still remain ungauged. The problem demands for modelling of stream flow. The traditional techniques of modelling not only require lengthy and reliable rainfall-runoff records, but also a procedure for updating the model parameters from time to time. Under such condition stream flow modelling is performed with reasonable accuracy by data driven artificial intelligence technique such as Artificial Neural Network. This book provides a comprehensive approach to stream flow modelling of Upper Kharun Catchment in Chhatttisgarh, India; involving two different methodologies of Artificial Neural Network. The book will be highly useful to the research scholars, academicians, hydrologists and water resources engineers.
| Autorius: | Jitendra Sinha, Avinash Agarwal, R. K. Sahu, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2013 |
| Knygos puslapių skaičius: | 168 |
| ISBN-10: | 3659474835 |
| ISBN-13: | 9783659474835 |
| Formatas: | 220 x 150 x 11 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „River Flow Modelling Using Artificial Neural Network: A Case Study Of Upper Kharun Catchment In Chhattisgarh, India“
