The Saline Terrain Prediction Model Based on Random Forest

With the development of science and technology, the trend of combining computers with terrain surveying is increasing. The Lop Nur Salt Field in Xinjiang has extremely rich mineral resources. In order to rationally develop and utilize salt lake resources, the establishment of a three-dimensional model for the distribution of salt field minerals is of great significance to the economic benefits and sustainable development of the enterprise. This paper adopts a data analysis method based on deep learning, and the proposed saline terrain prediction model first removes noise from the collected point cloud data, then combines multiple machine learning model training and testing sample data, and finally evaluates the experimental results through various performance indicators. The experimental results show that the accuracy of combining random forests with other network models is higher, and the mean square error value is 0.0021, which has the certain value for actual industrial development and production.