DOI: 10.1007/s11069-020-04394-x
论文题名: Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China
作者: Zhu Q. ; Luo Y. ; Zhou D. ; Xu Y.-P. ; Wang G. ; Tian Y.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2021
卷: 105, 期: 2 起始页码: 2161
结束页码: 2185
语种: 英语
中文关键词: Drought
; Remote sensing products
; Soil moisture
; Support vector machine (SVM)
; SWDI
英文关键词: data set
; drought
; prediction
; remote sensing
; soil moisture
; soil water
; support vector machine
; China
; Hunan
; Xiang Basin
; Varanidae
英文摘要: Droughts have caused many damages in many countries and might be aggravated around the world. Therefore, it is urgent to predict and monitor drought accurately. Soil moisture and its corresponding drought index (e.g., soil water deficit index, SWDI) are the key variables to define drought. However, in situ soil moisture observations are inaccessible in many areas. This study applies support vector machine (SVM) by using a new set of inputs to investigate the performance of in situ and remote sensing products (CMORPH-CRT, IMERG V05 and TRMM 3B42V7) for soil moisture and SWDI forecast over the Xiang River Basin. This study also assesses whether the addition of remote sensing soil moisture as input can improve the performance of SWDI prediction. The results are as follows: (1) the new set of inputs is suitable for drought prediction based on SVM; (2) using in situ precipitation as input to SVM shows the best performance for soil moisture prediction, which followed by TRMM 3B42V7, IMERG V05 and CMORPH-CRT; (3) in situ precipitation and IMERG V05 as input are more suitable for indirect SWDI prediction, while CMORPH-CRT and TRMM 3B42V7 are more suitable for direct SWDI prediction; (4) the addition of soil moisture with in situ precipitation or CMORPH-CRT both can improve the performance of direct SWDI prediction; (5) the lead time for drought prediction with SVM over the Xiang River Basin is about 2 weeks. © 2020, Springer Nature B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/169527
Appears in Collections: 气候变化与战略
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作者单位: School of Civil Engineering, Southeast University, Nanjing, 211189, China; Institute of Hydrology and Water Resources, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, 310058, China; State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing, 210029, China; Hydrology and Water Resources Department, Nanjing University of Information Science & Technology, Nanjing, 210044, China
Recommended Citation:
Zhu Q.,Luo Y.,Zhou D.,et al. Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China[J]. Natural Hazards,2021-01-01,105(2)