Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2018.05.022 |
Statistical downscaling of precipitation using machine learning techniques | |
Sachindra, D. A.1; Ahmed, K.2; Rashid, Md. Mamunur3; Shahid, S.4; Perera, B. J. C.1 | |
2018-11-01 | |
发表期刊 | ATMOSPHERIC RESEARCH
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2018 |
卷号 | 212页码:240-258 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia; Pakistan; USA; Malaysia |
英文摘要 | Statistical models were developed for downscaling reanalysis data to monthly precipitation at 48 observation stations scattered across the Australian State of Victoria belonging to wet, intermediate and dry climate regimes. Downscaling models were calibrated over the period 1950-1991 and validated over the period 1992-2014 for each calendar month, for each station, using 4 machine learning techniques, (1) Genetic Programming (GP), (2) Artificial Neural Networks (ANNs), (3) Support Vector Machine (SVM), and (4) Relevance Vector Machine (RVM). It was found that, irrespective of the climate regime and the machine learning technique, downscaling models tend to better simulate the average (compared to other statistics) and under-estimate the standard deviation and the maximum of the observed precipitation. Also, irrespective of the climate regime and the machine learning technique, at the majority of stations downscaling models showed an over-estimating trend of low to mid percentiles (i.e. below the 50th percentile) of precipitation and under-estimating trend of high percentiles of precipitation (i.e. above the 90th percentile). The over-estimating trend of low to mid percentiles of precipitation was more pronounced at stations located in dryer climate, irrespective of the machine learning technique. Based on the results of this investigation the use of RVM or ANN over SVM or GP for developing downscaling models can be recommended for a study such as flood prediction which involves the consideration of high extremes of precipitation. Also, RVM can be recommended over GP, ANN or SVM in developing downscaling models for a study such as drought analysis which involves the consideration of low extremes of precipitation. Furthermore, it was found that irrespective of the climate regime, the SVM and RVM-based precipitation downscaling models showed the best performance with the Polynomial kernel. |
英文关键词 | Statistical downscaling Machine learning Precipitation Australia Floods Droughts |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000439403300020 |
WOS关键词 | SUPPORT VECTOR MACHINE ; ARTIFICIAL NEURAL-NETWORK ; CIRCULATION MODEL OUTPUTS ; MULTIPLE LINEAR-REGRESSION ; CLIMATE-CHANGE PROJECTIONS ; RIVER-BASIN ; EXTREME TEMPERATURES ; WAVELET TRANSFORMS ; RAINFALL ; FLOW |
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/38513 |
专题 | 地球科学 |
作者单位 | 1.Victoria Univ, Coll Engn & Sci, Inst Sustainabil & Innovat, Footscray Pk Campus,POB 14428, Melbourne, Vic 8001, Australia; 2.Lasbela Univ Agr, Fac Water Resources Management Water & Marine Sci, Uthal, Balochistan, Pakistan; 3.Univ Cent Florida, Civil Environm & Construct Engn Dept, Orlando, FL 32816 USA; 4.Univ Teknol Malaysia, Fac Civil Engn, Johor Baharu 81310, Malaysia |
推荐引用方式 GB/T 7714 | Sachindra, D. A.,Ahmed, K.,Rashid, Md. Mamunur,et al. Statistical downscaling of precipitation using machine learning techniques[J]. ATMOSPHERIC RESEARCH,2018,212:240-258. |
APA | Sachindra, D. A.,Ahmed, K.,Rashid, Md. Mamunur,Shahid, S.,&Perera, B. J. C..(2018).Statistical downscaling of precipitation using machine learning techniques.ATMOSPHERIC RESEARCH,212,240-258. |
MLA | Sachindra, D. A.,et al."Statistical downscaling of precipitation using machine learning techniques".ATMOSPHERIC RESEARCH 212(2018):240-258. |
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