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Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning

文献类型：期刊论文

作者Ma, Yuju1; Zuo, Liyuan2,3; Gao, Jiangbo3; Liu, Qiang1; Liu, Lulu3
刊名ATMOSPHERE
出版日期2021-10-01
卷号12期号:10页码:16
关键词karst NDVI natural and anthropogenic factors BPNN RBFNN RF SVR prediction comparison
DOI10.3390/atmos12101341
通讯作者Gao, Jiangbo(gaojiangbo@igsnrr.ac.cn)
英文摘要As a link for energy transfer between the land and atmosphere in the terrestrial ecosystem, karst vegetation plays an important role. Karst vegetation is not only affected by environmental factors but also by intense human activities. The nonlinear characteristics of vegetation growth are induced by the interaction mechanism of these factors. Previous studies of this relationship were not comprehensive, and it is necessary to further explore it using a suitable method. In this study, we selected climate, human activities, topography, and soil texture as the response factors; a nonlinear relationship model between the karst normalized difference vegetation index (NDVI) and these factors was established by applying a back propagation neural network (BPNN), a radial basis function neural network (RBFNN), the random forest (RF) algorithm, and support vector regression (SVR); and then, the karst NDVI was predicted. The coefficient of determination (R-2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the obtained results were calculated, and the mean R-2 values of the BPNN, RBFNN, RF, and SVR models were determined to be 0.77, 0.86, 0.89, and 0.91, respectively. Compared with the BPNN, RBFNN, and RF models, the SVR model had the lowest errors, with mean MSE, RMSE, and MAPE values of 0.001, 0.02, and 2.77, respectively. The results show that the BPNN, RBFNN, RF, and SVR models are within acceptable ranges for karst NDVI prediction, but the overall performance of the SVR model is the best, and it is more suitable for karst vegetation prediction.
WOS关键词CLIMATE-CHANGE ; VEGETATION COVER ; CHINA ; STRESS
资助项目National Natural Science Foundation of China[42071288] ; National Natural Science Foundation of China[41671098] ; Programme of Kezhen-Bingwei Excellent Young Scientists of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[2020RC002] ; National Key Research and Development Program of China[2018YFC1509002]
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
语种英语
WOS记录号WOS:000747074500001
出版者MDPI
资助机构National Natural Science Foundation of China ; Programme of Kezhen-Bingwei Excellent Young Scientists of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; National Key Research and Development Program of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/170481]  
专题中国科学院地理科学与资源研究所
通讯作者Gao, Jiangbo
作者单位1.Ocean Univ China, Coll Engn, Qingdao 266100, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Yuju,Zuo, Liyuan,Gao, Jiangbo,et al. Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning[J]. ATMOSPHERE,2021,12(10):16.
APA Ma, Yuju,Zuo, Liyuan,Gao, Jiangbo,Liu, Qiang,&Liu, Lulu.(2021).Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning.ATMOSPHERE,12(10),16.
MLA Ma, Yuju,et al."Comparing Four Types Methods for Karst NDVI Prediction Based on Machine Learning".ATMOSPHERE 12.10(2021):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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