Theme-7-2-Drought analysis and synthetic generation.

dc.contributor.author	Majumdar, Mandakinee
dc.date.accessioned	2019-09-17T10:34:36Z
dc.date.available	2019-09-17T10:34:36Z
dc.date.issued	1988
dc.identifier.uri	http://117.252.14.250:8080/jspui/handle/123456789/3578
dc.description.abstract	Synthetic data generation on two Indian rivers, Damodar and Cauveri was carried out using ARIMA (1,0,1) and fast fractional Gaussian noise model (ffGn). The quality of generated data regarding preserving the historical basic properties like mean, variance as well as long term properties like Hurst Coefficient (H) and run lengths of low flow generation as depicted by drought curve was investigated. The results for both type generation suggest that ffGn has a better capability of generating long low flow sequences than ARIMA model.	en_US
dc.language.iso	en	en_US
dc.publisher	National Institute of Hydrology	en_US
dc.subject	Synthetic data generation	en_US
dc.title	Theme-7-2-Drought analysis and synthetic generation.	en_US
dc.type	Technical Report	en_US