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5-Drought Forecasting Using Standardized Precipitation Index.

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dc.contributor.author Desai, V. R.
dc.contributor.author Mishra, A. K.
dc.date.accessioned 2022-06-15T21:23:34Z
dc.date.available 2022-06-15T21:23:34Z
dc.date.issued 2006
dc.identifier.uri http://117.252.14.250:8080/jspui/handle/123456789/6864
dc.description.abstract Drought forecasting plays an important role in the planning and management of natural resources including water resource-systems of a river basin, since drought has severe effect when it persists over a longer period Modeling and forecasting of droughts, which are nonlinear and non-stationary, is a complex exercise. During the last decade neural networks have shown great ability in modeling and forecasting nonlinear and non- stationary time series. In this study an application of the back propagation feed forward recursive ANN models are presented to forecast droughts., The models were applied to forecast droughts using standardized precipitation index series_as 'drought indices in the Kansabati River Basin, which lies in the Purulia District of West Bengal. The resulting trained network is capable of forecasting with satisfactory results upto 2-months of lead time. The model can be used for water resource management in the river basin en_US
dc.language.iso en en_US
dc.publisher National Institute of Hydrology en_US
dc.subject Kansabati River Basin en_US
dc.subject Neural networks en_US
dc.subject Standardized precipitation index en_US
dc.subject Drought indices en_US
dc.title 5-Drought Forecasting Using Standardized Precipitation Index. en_US
dc.type Technical Report en_US


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