Rainfall-Runoff Modeling Using Artificial Neural Networks
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BeschreibungThe book addresses a two-pronged approach for the determination of a watershed's response by developing a physically-based model and a neural network-based model. For the physically-based model, the watershed is partitioned into a series of one-dimensional 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 network-based 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 medium-sized watersheds - El Dorado, Marion, and Council Grove in Kansas, USA. The prediction of watershed response was also studied using several existing empirical rainfall-runoff models. The advantage of ANNs over the physically-based models is that they require only input and output data for mapping of an unknown function such as rainfall-runoff relationship. In the case of physically-based models a lot more data is required.
PortraitDr. Jagadeesh Anmala, PhD: Obtained B.Tech, MS, PhD from IIT Bombay (Mumbai), Kansas State University, Georgia Institute of Technology. He is currently working as Assistant Professor at Birla Institute of Technology and Sciences, Pilani, Hyderabad Campus.
Untertitel: Rainfall-Runoff Modeling Using Artificial Neural Networks(ANNs) and Physically-based Model-Theory Simulation and Results. Paperback. Sprache: Englisch.
Verlag: LAP Lambert Acad. Publ.
Erscheinungsdatum: Juli 2010
Seitenanzahl: 200 Seiten