Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/14180
Full metadata record
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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Salim DJERBOUAI.pdf | 369,46 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.