Research on Prediction of Illegal Wildlife Trade Based on PCA and Multiple Linear Regression

Illegal wildlife trade is a long-standing issue, exerting negative impacts over national economies, regional security, and even global ecosystems. Governments worldwide and numerous environmental and animal protection organizations have been deeply engaged in addressing this issue for many years. Historically, there has been a lack of a quantifiable model to analyze the impact of implementing wildlife monitoring and conservation projects using drones on illegal wildlife trade.In this paper, we primarily propose an Illegal Wildlife Trade Prediction Model. By integrating four secondary indicators related to wildlife trade with the aforementioned 16 indicators, we reduced them to four primary indicators. The methodological tests confirmed their strong correlation, and multiple regression analysis was used to predict illegal wildlife trade, thereby verifying the project's effectiveness. Innovatively, we established a model to measure the probability of project completion, combining data from model predictions, resulting in an approximate completion probability of 60%. In conducting a project sensitivity analysis, we also developed a new model to perturb the three primary indicators, predicting the project's final outcome under random fluctuation of indicators, with variation rates as low as 0.34%, 0.98%, and 1.22%, indicating the project's stability.