Using Support Vector Machine Regression Algorithm to Evaluate the Impact of Urbanization on Ecological Environment

This article evaluates the impact of urbanization on the ecological environment through the Support Vector Regression (SVR) algorithm. Firstly, an evaluation model was established by collecting and analyzing data on multiple key indicators and natural environmental parameters far from rural areas. This study uses the SVR algorithm to train and validate the model, in order to predict the impact of urbanization on the ecological environment at different stages. During the experimental phase, four different experiments were conducted to test and evaluate the performance and applicability of the model. In the benchmark model comparison experiment, the mean squared error (MSE) based on the SVR model was 12.5, and the coefficient of determination (R2) was 0.89, the MSE value of linear regression was 18.3, the value of R2 was 0.75, the MSE value of the decision tree was 20.1, the value of R2 was 0.70, MSE value of random forest was 14.2, the value of R2 was 0.82. In the sensitivity analysis experiment of data features, the MSE value increased to 18.4 after removing the transportation development index, while the MSE value was 15.3 when all indicators were complete. In the experiment of testing the impact of noise data, as the noise level increased from 0% to 20%, the MSE value increased from 14.2 to 23.7. In cross regional generalization ability testing experiments, it may be necessary to adjust the SVR model or adopt more flexible models to adapt to specific environments in different regions in practical applications. From the experimental data conclusion, it can be seen that the SVR model has good performance and adaptability in predicting the natural environment far from rural areas, but still faces challenges in dealing with data noise and cross regional generalization.