Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2019.104806 |
Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms | |
Ahmed, Kamal1,2; Sachindra, D. A.3; Shahid, Shamsuddin1; Iqbal, Zafar1; Nawaz, Nadeem2; Khan, Najeebullah1 | |
2020-05-15 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2020 |
卷号 | 236 |
文章类型 | Article |
语种 | 英语 |
国家 | Malaysia; Pakistan; Australia |
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 (T-max) and minimum (T-min) 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 intercomparison 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. |
英文关键词 | General circulation models Multi-model ensemble Taylor skill score Machine learning algorithms Temperature and precipitation Pakistan |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000525322900017 |
WOS关键词 | ARTIFICIAL NEURAL-NETWORK ; TEMPORAL-CHANGES ; CLIMATE MODELS ; CMIP5 MODELS ; PROJECTION ; RAINFALL ; SELECTION ; SKILL ; INDIA ; GCMS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/278882 |
专题 | 地球科学 |
作者单位 | 1.UTM, Sch Civil Engn, Johor Baharu 81310, Malaysia; 2.Lasbela Univ Agr Water & Marine Sci, Fac Engn Sci & Technol, Balochistan, Pakistan; 3.Victoria Univ, Coll Engn & Sci, Inst Sustainabil & Innovat, POB 14428, Melbourne, Vic 8001, Australia |
推荐引用方式 GB/T 7714 | Ahmed, Kamal,Sachindra, D. A.,Shahid, Shamsuddin,et al. Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms[J]. ATMOSPHERIC RESEARCH,2020,236. |
APA | Ahmed, Kamal,Sachindra, D. A.,Shahid, Shamsuddin,Iqbal, Zafar,Nawaz, Nadeem,&Khan, Najeebullah.(2020).Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms.ATMOSPHERIC RESEARCH,236. |
MLA | Ahmed, Kamal,et al."Multi-model ensemble predictions of precipitation and temperature using machine learning algorithms".ATMOSPHERIC RESEARCH 236(2020). |
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