Simulation of vegetation change based on BP-SVM mode
Received date: 2020-12-15
Revised date: 2021-02-10
Online published: 2021-08-03
Vegetation is an important link that connects the biosphere, atmosphere and hydrosphere, and has an important impact on the watershed in the ecological environment and on the exchange of water and heat. Previous studies focused on correlation analyses between the normalized difference vegetation index (NDVI) and climate factors, but the lag and the increase of forecast factors were rarely used to improve the precision of the NDVI model. Thus, this article compared the prediction performance of the multiple linear regression, artificial neural network and support vector machine, then selected the model with the highest precision. Using conventional factors (rainfall and temperature), the prediction performance was tested when soil moisture and sunshine duration were increased, which affect vegetation growth, and the time lag effect between the different factors and NDVI were considered. (1) SVM had the strongest fitting ability and the highest NDVI prediction accuracy. The root-mean-square error of the Jing River Basin and the Beiluo River Basin were reduced by more than 1.8%. (2) The root-mean-square error (RMSE) of Jing River Basin decreased by up to 8.8% when soil moisture, sunshine and other factors were added. (3) Considering the lag time, the root-mean-square error of the Jing River Basin and the Beiluo River Basin decreased by 15% and 11%, respectively, and the predicting accuracy of NDVI was improved further, thus increasing the reliability of the model. These prediction results have an important reference significance for the formulation of ecological protection strategies and the guidance of ecological restoration in the future.
JIA Songtao,HUANG Shengzhi,WANG Hao,LI Ziyan,HUANG Qiang,LIANG Hao . Simulation of vegetation change based on BP-SVM mode[J]. Arid Zone Research, 2021 , 38(4) : 1085 -1093 . DOI: 10.13866/j.azr.2021.04.20
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