Comparing machine learning methods for predicting land development intensity.

ABSTRACT: Land development intensity is a comprehensive indicator to measure the degree of saving and intensive land construction and economic production activities. It is also the result of the joint action of natural, social, economic, and ecological elements in land development and utilization. Scientific prediction of land development intensity has particular reference significance for future regional development planning and the formulation of reasonable land use policies. Based on the inter-provincial land development intensity and its influencing factors in China, this study applied four algorithms, XGBoost, random forest model, support vector machine, and decision tree, to simulate and predict the land development intensity, and then compared the prediction accuracy of the four algorithms, a

SUBMITTER: Gu G 

PROVIDER: S-EPMC10075418 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature