Comparative Analysis of Machine Learning Algorithms for Elevation Dataset Regression

The study explores the importance of elevation datasets in various geospatial applications, such as environmental modeling and infrastructure planning. A thorough regression analysis on elevation datasets utilizing Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, and Decision Tree algorithms is conducted. The research study aims to evaluate their effectiveness of different algorithms in predicting the elevation values across various terrains.. The study covers data cleaning, feature scaling, and dataset partitioning. Each algorithm is applied to the training data to create regression models, which are then evaluated on the training set using metrics such as Mean Squared Error (MSE) and R-Squared. The result highlights the strengths and weakness of each algorithm.