Prediction Model and Demonstration of Regional Agricultural Carbon Emissions Based on PLS-SA-AdaBoost: A Case Study of Fujian Province, China

The prediction of regional agricultural carbon emissions is of great significance to regional environmental protection and sustainable development of regional agriculture. This paper puts forward a combined prediction model integrating Partial Least Squares (PLS), Simulated Annealing (SA) and Adaptive Boosting (AdaBoost) to predict regional agricultural carbon emissions, which overcomes the shortcomings of insufficient accuracy of a single model prediction. This paper conducts a demonstrative study on the agricultural carbon emissions in Fujian Province, China to verify the feasibility and effectiveness of the PLS-SA-AdaBoost combined prediction model. The experimental results show that PLS-SA-AdaBoost combined prediction model has a higher precision than SA-AdaBoost model and PLS-SA-AdaBoost model; meanwhile PLS-SA-AdaBoost combined prediction model shows obvious advantages compared with other combined prediction models. In terms of five different scenarios, the paper adopts PLS-SA-AdaBoost combined prediction model to predict the future trend of agricultural carbon emissions in Fujian Province.