Drought forecasting using feed-forward neural network

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DC Field	Value	Language
dc.contributor.author	Sarah, HELLASSA	-
dc.contributor.author	Doudja, SOUAG	-
dc.contributor.author	Salim, DJERBOUAI	-
dc.date.accessioned	2017-06-13T09:27:39Z	-
dc.date.available	2017-06-13T09:27:39Z	-
dc.date.issued	2017-06-13	-
dc.identifier.issn	sa	-
dc.identifier.uri	http://dspace.univ-ouargla.dz/jspui/handle/123456789/14180	-
dc.description	الملتقى الدولي الثاني حول: الموارد المائية'' تقييم و إقتصاد وحماية'' یومي الاثنین والثلاثاء 21 و22 دیسمبر 2016	en_US
dc.description.abstract	Drought forecasting is a major component of a drought preparedness and mitigation plan. This study compares linear stochastic models (ARIMA/SARIMA) and Feed-forward neural network (FFNN) models for drought forecasting in the Algerois catchment in Algeria, using standardized precipitation index (SPI) as a drought quantifying parameter. The results obtained from two models are presented in this paper.	en_US
dc.description.sponsorship	Salim DJERBOUAI	en_US
dc.language.iso	en	en_US
dc.relation.ispartofseries	2016;	-
dc.subject	Drought	en_US
dc.subject	ARIMA	en_US
dc.subject	SARIMA	en_US
dc.subject	FFNN	en_US
dc.subject	Forecasting	en_US
dc.title	Drought forecasting using feed-forward neural network	en_US
dc.type	Article	en_US
Appears in Collections:	3. Faculté des sciences appliquées

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