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