Forest Cover Type Classification Using Machine Learning Algorithms

It is important to understand the different types of forest cover because forests are critical for decreasing air pollution, controlling greenhouse gas emissions, and preserving ecological equilibrium. So, it becomes very important to understand the types of trees present in different parts of the forest. So, using machine learning techniques, a structure is developed to entirely automate this procedure. A variety of classification algorithms were used to classify different forest cover types based on cartographic features. Supervised machine learning algorithms were used. Classification algorithms used in this study were Decision Tree Classifier, Multinomial Logistic Regression, k-Nearest Neighbors Classifier, and Random Forest Classifier. Different performance criteria were performed to measure each classifier’s performance. The Random Forest classifier outperforms the other three classifiers in this study. It helps the users classify the cover type easily without having to deal with all that data. In the end, it is integrated with the Flask framework. With the help of this classification, further research can be carried out regarding the inhabitants of forests, deforestation, forest fires, and many other concerns.