基于BP-SVM模型的植被变化模拟研究

doi:10.13866/j.azr.2021.04.20

Abstract

Abstract:

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.

Key words: support vector machines, prediction accuracy, prediction model, time-lag effect, vegetation coverage

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.

Figures/Tables 8

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References 30

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研究区域	类别	多元线性回归				人工神经网络				支持向量机
研究区域	类别	纳什系数	均方根误差	平均相对误差		纳什系数	均方根误差	平均相对误差		纳什系数	均方根误差	平均相对误差
泾河	训练期	0.820	0.066	0.203		0.936	0.038	0.118		0.928	0.040	0.115
泾河	验证期	0.731	0.092	0.224		0.750	0.088	0.183		0.761	0.086	0.177
北洛河	训练期	0.832	0.077	0.187		0.953	0.040	0.092		0.953	0.040	0.083
北洛河	验证期	0.544	0.128	0.205	0.611		0.120	0.191	0.663		0.103	0.184

研究区域	模型类别	降雨气温两因子				降雨、气温、土壤湿度、日照多因子
研究区域	模型类别	纳什系数	均方根误差	平均相对误差		纳什系数	均方根误差	平均相对误差
泾河	训练期	0.928	0.040	0.115		0.942	0.036	0.099
泾河	验证期	0.761	0.086	0.177		0.729	0.079	0.167
北洛河	训练期	0.953	0.040	0.083		0.959	0.041	0.084
北洛河	验证期	0.663	0.103	0.184	0.670		0.118	0.188

研究区域	气候因子	滞后时间/月
研究区域	气候因子	0	1	2	3	4	5	6	7	8	9	10	11	12
泾河	NDVI	-	0.80	0.42	0.01	-0.42	-0.78	-0.90	-0.78	-0.41	0.01	0.42	0.78	0.97
	降雨	0.90	0.88	0.62	0.19	-0.30	-0.70	-0.91	-0.88	-0.62	-0.18	0.31	0.69	0.90
	气温	0.68	0.60	0.32	-0.04	-0.35	-0.59	-0.69	-0.61	-0.37	0.01	0.37	0.59	0.68
	土壤湿度	-0.15	-0.32	-0.41	-0.38	-0.23	-0.05	0.15	0.33	0.43	0.41	0.26	0.03	-0.20
	日照	0.20	0.33	0.35	0.31	0.17	0.01	-0.17	-0.30	-0.38	-0.36	-0.24	-0.02	0.18
北洛河	NDVI	-	0.42	0.00	-0.43	-0.79	-0.92	-0.78	-0.42	0.01	0.43	0.79	0.98	0.79
	降雨	0.90	0.87	0.61	0.17	-0.31	-0.71	-0.92	-0.88	-0.61	-0.16	0.32	0.71	0.90
	气温	0.75	0.64	0.33	-0.08	-0.41	-0.64	-0.73	-0.63	-0.38	0.03	0.42	0.66	0.75
	土壤湿度	-0.20	-0.37	-0.45	-0.40	-0.22	-0.01	0.17	0.33	0.42	0.40	0.26	0.01	-0.22
	日照	0.16	0.30	0.32	0.29	0.15	-0.01	-0.15	-0.25	-0.32	-0.31	-0.21	-0.02	0.14

研究区域	类别	考虑滞时的预测模型				不考虑滞时的预测模型
研究区域	类别	纳什系数	均方根误差	平均相对误差		纳什系数	均方根误差	平均相对误差
泾河	训练期	0.982	0.020	0.048		0.940	0.037	0.103
泾河	验证期	0.880	0.062	0.140		0.787	0.082	0.183
北洛河	训练期	0.989	0.020	0.038		0.947	0.042	0.085
北洛河	验证期	0.927	0.056	0.112	0.687		0.115	0.185

Simulation of vegetation change based on BP-SVM mode

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