干旱区生态环境知识资源中心(Arid): Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts

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DOI	10.1007/s10661-019-7991-1
	Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts
	Mehr, Ali Danandeh 1; Safari, Mir Jafar Sadegh 2
通讯作者	Mehr, Ali Danandeh
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2020
卷号	192 期号:1
英文摘要	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 machine learning model, namely multiple genetic programming (MGP), that improves the predictive accuracy of the standalone genetic programming (GP) technique when used for 1-month ahead rainfall forecasting. The new model uses a multi-step evolutionary search algorithm in which high-performance rain-borne genes from a multigene GP solution are recombined through a classic GP engine. The model is demonstrated using rainfall measurements from two meteorology stations in Lake Urmia Basin, Iran. The efficiency of the MGP was cross-validated against the benchmark models, namely standard GP and autoregressive state-space. The results indicated that the MGP statistically outperforms the benchmarks at both rain gauge stations. It may reduce the absolute and relative errors by approximately up to 15% and 40%, respectively. This significant improvement over standalone GP together with the explicit structure of the MGP model endorse its application for 1-month ahead rainfall forecasting in practice.
英文关键词	Rainfall Stochastic modelling Genetic programming Hybrid models
类型	Article
语种	英语
国家	Turkey
收录类别	SCI-E
WOS记录号	WOS:000511311100010
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; STOCHASTIC-MODELS ; PREDICTION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314436
作者单位	1.Antalya Bilim Univ, Dept Civil Engn, Antalya, Turkey; 2.Yasar Univ, Dept Civil Engn, Izmir, Turkey
推荐引用方式 GB/T 7714	Mehr, Ali Danandeh,Safari, Mir Jafar Sadegh. Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts[J],2020,192(1).
APA	Mehr, Ali Danandeh,&Safari, Mir Jafar Sadegh.(2020).Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts.ENVIRONMENTAL MONITORING AND ASSESSMENT,192(1).
MLA	Mehr, Ali Danandeh,et al."Multiple genetic programming: a new approach to improve genetic-based month ahead rainfall forecasts".ENVIRONMENTAL MONITORING AND ASSESSMENT 192.1(2020).