Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts

dc.contributor.author	Danandeh Mehr, A.
dc.contributor.author	Safari, M.J.S.
dc.date.accessioned	2021-01-25T20:48:05Z
dc.date.available	2021-01-25T20:48:05Z
dc.date.issued	2020
dc.identifier	10.1007/s10661-019-7991-1
dc.identifier.issn	01676369
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076389966&doi=10.1007%2fs10661-019-7991-1&partnerID=40&md5=fe358773b370d2a3b26707fe051fe2ca
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/9727
dc.description.abstract	It is well documented that standalone machine learning methods are not suitable for rainfall forecasting in long lead-time horizons. The task is more difficult in arid and semiarid regions. Addressing these issues, the present paper introduces a hybrid ma
dc.language.iso	English
dc.publisher	Environmental Monitoring and Assessment
dc.title	Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts
dc.type	Article
dc.relation.volume	192
dc.relation.issue	1
dc.description.affiliations	Department of Civil Engineering, Antalya Bilim University, Antalya, Turkey; Department of Civil Engineering, Yaşar University, Izmir, Turkey

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