气候变化领域集成服务门户(CCPortal): Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms

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DOI	10.1016/j.atmosres.2019.104806
	Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms
	Ahmed K.; Sachindra D.A.; Shahid S.; Iqbal Z.; Nawaz N.; Khan N.
发表日期	2020
ISSN	0169-8095
卷号	236
英文摘要	Multi-Model Ensembles (MMEs) are often employed to reduce the uncertainties related to GCM simulations/projections. The objective of this study was to evaluate the performance of MMEs developed using machine learning (ML) algorithms with different combinations of GCMs ranked based on their performance and determine the optimum number of GCMs to be included in an MME. In this study ML algorithms; Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Relevance Vector Machine (RVM) were used to develop MMEs for annual, monsoon and winter; precipitation (P), maximum (Tmax) and minimum (Tmin) temperature over Pakistan using 36 Coupled Model Intercomparison Project Phase 5 GCMs. GCMs were ranked using Taylor Skill Score for individual seasons and variables, and then using a comprehensive Rating Metric (RM) overall rank of each GCM was determined. It was found that, HadGEM2-AO is the most skilled GCM and IPSL-CM5B-LR is the least skilled GCMs in simulating the 3 climate variables. The performance of MMEs did not improve after the inclusion of about 18 top-ranked GCMs. Thus, it was understood that the optimum performance of MMEs is achieved when about 50% of the top-ranked GCMs are used. The inter-comparison of MMEs developed with ANN, KNN, SVM and RVM revealed that KNN and RVM-based MMEs show better skills. It was found that RVM yields MMEs which show smaller variations in performance over space unlike ANN which displayed large fluctuations in performance over space. KNN and RVM are recommended over SVM and ANN for the development of MMEs over Pakistan. © 2019 Elsevier B.V.
英文关键词	General circulation models; Machine learning algorithms; Multi-model ensemble; Pakistan; Taylor skill score; Temperature and precipitation
学科领域	Learning systems; Machine learning; Nearest neighbor search; Neural networks; Support vector machines; Coupled Model Intercomparison Project; General circulation model; K nearest neighbours (k-NN); Multi-model ensemble; Optimum performance; Pakistan; Relevance Vector Machine; Skill Score; Learning algorithms; air temperature; algorithm; climate modeling; climate prediction; ensemble forecasting; general circulation model; machine learning; precipitation assessment; Pakistan
语种	英语
scopus关键词	Learning systems; Machine learning; Nearest neighbor search; Neural networks; Support vector machines; Coupled Model Intercomparison Project; General circulation model; K nearest neighbours (k-NN); Multi-model ensemble; Optimum performance; Pakistan; Relevance Vector Machine; Skill Score; Learning algorithms; air temperature; algorithm; climate modeling; climate prediction; ensemble forecasting; general circulation model; machine learning; precipitation assessment; Pakistan
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120477
作者单位	School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia; Faculty of Engineering Sciences & Technology, Lasbela University of Agriculture, Water and Marine SciencesBalochistan, Pakistan; Institute for Sustainability and Innovation, College of Engineering and Science, Victoria University, P.O. Box 14428, Melbourne, Victoria 8001, Australia
推荐引用方式 GB/T 7714	Ahmed K.,Sachindra D.A.,Shahid S.,et al. Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms[J],2020,236.
APA	Ahmed K.,Sachindra D.A.,Shahid S.,Iqbal Z.,Nawaz N.,&Khan N..(2020).Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms.Atmospheric Research,236.
MLA	Ahmed K.,et al."Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms".Atmospheric Research 236(2020).