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dc.contributor.authorMehr, Ali Danandeh
dc.contributor.authorVaheddoost, Babak
dc.contributor.authorMohammadi, Babak
dc.date.accessioned2021-03-20T20:09:15Z
dc.date.available2021-03-20T20:09:15Z
dc.date.issued2020
dc.identifier.issn0098-3004
dc.identifier.issn1873-7803
dc.identifier.urihttp://doi.org/10.1016/j.cageo.2020.104622
dc.identifier.urihttps://hdl.handle.net/20.500.12885/332
dc.descriptionWOS:000587352100016en_US
dc.description.abstractThis paper presents a new hybrid model, called ENN-SA, for spatiotemporal drought prediction. In ENN-SA, an Elman neural network (ENN) is conjugated with simulated annealing (SA) optimization and support vector machine (SVM) classification algorithms for the standardized precipitation index (SPI) modeling at multiple stations. The proposed model could be applied to predict SPI at different time scales in a meteorology station with lack of data through the intelligent use of SPI series of the nearby stations as the model inputs. The capability of the hybrid model for multi-station prediction of meteorological drought was examined through the cross-validation technique for Kecioren station in Ankara Province, Turkey. To this end, the SPI-3, SPI-6, and SPI-12 at the station were modeled using the same indices of five nearby stations. In the first step, SVM was trained using different kernels in order to generate and classify a set of plausible multi-station prediction scenarios. Then, ENN was used to regress the SPI series at each scenario and finally, the SA component of the integrated model was utilized to improve the ENN efficiency. Various error and complexity measures were used to detect the models' performance. The results showed the ENN-SA is promising and efficient for multi-station SPI prediction.en_US
dc.language.isoengen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Geosciencesen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlgorithmsen_US
dc.subjectGeostatisticsen_US
dc.subjectElman neural networksen_US
dc.subjectData processingen_US
dc.subjectHydrologyen_US
dc.titleENN-SA: A novel neuro-annealing model for multi-station drought predictionen_US
dc.typearticleen_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.authorid0000-0002-4767-6660en_US
dc.contributor.institutionauthorVaheddoost, Babak
dc.identifier.doi10.1016/j.cageo.2020.104622en_US
dc.identifier.volume145en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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