Analysis of prediction algorithm for forest land spatial evolution trend in rural planning

Abstract

This study is to find the factors that affect the spatial change of forest land and purposefully predict the evolution trend of forest land space, so as to facilitate the rural planning work. The rural forest land situation in Zhangjiakou City of Hebei Province is analyzed, and the future evolution and development of forest space are predicted through analysis of correlation between the forest land influencing factors and the forest land productivity. Meanwhile, the multiple linear regression (MLR) prediction algorithm and support vector machine (SVM) are compared to obtain a more accurate prediction algorithm, which provides a strong basis for rural planning. The research results show that the annual rainfall and rainfall erosion have poor correlation with the spatial evolution of forest land relatively; while the average annual temperature is negatively correlated with annual rainfall and the rainfall erosivity. In addition, the soil erosion and terrain undulation of forest land have higher correlations with the rainfall erosivity due to abundant rainfall. The steeper the slope, the less human interference. What’s more, the prediction value of SVM is closer to the actual value with smaller absolute error, so it is more accurate than MLR. Therefore, research on the prediction algorithm provides new ideas for enriching the prediction algorithms of the spatial evolution trend, and is of great significance for improving the forest resource reserve capacity and meeting more forest resource demand in China. In addition, it can optimize the natural environmental quality, so it can be applied to rural planning and construction.