Title
RainfallRunoff Modeling Using Artificial Neural Networks: RainfallRunoff Modeling Using Artificial Neural Networks(ANNs) and P,Used
Delivery time: 8-12 business days (International)
The book addresses a twopronged approach for the determination of a watershed's response by developing a physicallybased model and a neural networkbased model. For the physicallybased model, the watershed is partitioned into a series of onedimensional overland flow planes and channel elements, and water is routed over these elements in a cascading fashion. A system of partial differential equations under the kinematic wave approximation was used to describe surface water movement. The applicability of ANNs was investigated by developing a neural networkbased runoff predictive model. The performance of ANNs, with different architectures, was evaluated using monthly precipitation and temperature data (input) and watershed runoff (output) for 3 mediumsized watersheds ? El Dorado, Marion, and Council Grove in Kansas, USA. The prediction of watershed response was also studied using several existing empirical rainfallrunoff models. The advantage of ANNs over the physicallybased models is that they require only input and output data for mapping of an unknown function such as rainfallrunoff relationship. In the case of physicallybased models a lot more data is required.
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⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
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We provide a 2-year limited warranty, from the date of purchase for all our products.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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