Computational Intensity Prediction Model of Vector Data Overlay with Random Forest Method

摘要

Spatial analysis is the core of geographic information system(GIS),of which,spatial overlay of vector data is a major job.Computational intensity of the spatial overlay has a direct effect on the overall performance of the GIS.High precision modeling for the computational intensity and analysis of the vector data overlay has been a challenging task.Thus,the paper proposes a novel approach,which utilizes self-learning and self-training features of optimized random forest algorithm to the vector data overlay analysis.Simulation experiments show that the proposed model is superior to non-optimized random forest algorithm and support vector machine model with higher prediction precision and is also capable of eliminate redundant computational intensity features.