DOI: 10.1016/j.atmosres.2018.05.001
Scopus记录号: 2-s2.0-85047260176
论文题名: Improvement of rainfall estimation from MSG data using Random Forests classification and regression
作者: Ouallouche F. ; Lazri M. ; Ameur S.
刊名: Atmospheric Research
ISSN: 1698095
出版年: 2018
卷: 211 起始页码: 62
结束页码: 72
语种: 英语
英文关键词: Learning machine
; Meteosat Second Generation (MSG)
; Rainfall estimation
; Random Forest (RF)
Scopus关键词: Decision trees
; Image enhancement
; Learning systems
; Rain gages
; Regression analysis
; Classification performance
; Learning machines
; Meteosat second generation spinning enhanced visible and infrared imager (seviri) (MSG)
; Meteosat second generations
; Predictor variables
; Rainfall estimations
; Random forests
; Statistical parameters
; Rain
英文摘要: In this study, a new rainfall estimation technique on 3 h and 24 h scales applied in Northern Algeria is presented. The proposed technique is based on Random Forests (RF) algorithm using data retrieved from Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Because the rain rate depended on the precipitation type: convective or stratiform, the RF technique is divided into two stages. The first is the classification of rainfall into three classes (no-rain, convective and stratiform) using RF classification and the second consists in assigning rain rate to the pixels belonging to the two classes (convective and stratiform) using RF regression. In classification step, spectral, textural and temporal features of clouds are used as predictor variables and the results are validated against co-located rainfall classes observed by radar. The statistical parameters score shows stronger rainfall classification performance for RF compared to the ANN and SVM. The RF regression model is validated by comparison with against co-located rainfall rates measured by a rain gauge. The results show rain rates estimated by the developed scheme are in good correlation with those observed by rain gauges. © 2018 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/108836
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: LAMPA Laboratory (Laboratoire d'Analyse et de Modélisation des Phénomènes Aléatoires), Mouloud MAMMERI University of Tizi Ouzou, Algeria
Recommended Citation:
Ouallouche F.,Lazri M.,Ameur S.. Improvement of rainfall estimation from MSG data using Random Forests classification and regression[J]. Atmospheric Research,2018-01-01,211