DOI: 10.1016/j.atmosres.2018.05.022
Scopus记录号: 2-s2.0-85047809417
论文题名: Statistical downscaling of precipitation using machine learning techniques
作者: Sachindra D.A. ; Ahmed K. ; Rashid M.M. ; Shahid S. ; Perera B.J.C.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 212 起始页码: 240
结束页码: 258
语种: 英语
英文关键词: Australia
; Droughts
; Floods
; Machine learning
; Precipitation
; Statistical downscaling
Scopus关键词: Drought
; Floods
; Genetic algorithms
; Genetic programming
; Learning algorithms
; Learning systems
; Neural networks
; Precipitation (chemical)
; Support vector machines
; Australia
; Drought analysis
; Machine learning techniques
; Other statistics
; Polynomial kernels
; Relevance Vector Machine
; Standard deviation
; Statistical downscaling
; Climate models
; artificial neural network
; downscaling
; drought
; flood
; machine learning
; precipitation assessment
; support vector machine
; Australia
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. © 2018 Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108814
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
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作者单位: Institute for Sustainability and Innovation, College of Engineering and Science, Victoria University, P.O. Box 14428, Melbourne, Victoria 8001, Australia; Faculty of Water Resources Management, Water and Marine Sciences, Lasbela University of Agriculture, Uthal, Balochistan, Pakistan; Civil, Environmental, and Construction Engineering Department, University of Central Florida, Orlando, Florida, 32816-2450, United States; Faculty of Civil Engineering, Universiti Teknologi Malaysia, Johor Bahru, 81310, Malaysia
Recommended Citation:
Sachindra D.A.,Ahmed K.,Rashid M.M.,et al. Statistical downscaling of precipitation using machine learning techniques[J]. Atmospheric Research,2018-01-01,212