An Analysis of Machine Learning Techniques for Forest Cover Type Classification

For environmental management and conservation, forecasting the forest's tree cover is essential. Accurate prediction models are needed to evaluate the possible effects and pinpoint places that need rapid attention as deforestation and forest degradation become a more widespread concern. In this study, we employ machine learning to predict forest tree cover using dataset of environmental variables and historical tree cover data. The foundation of our approach is the Random Forests ensemble learning technique, which combines different decision trees to improve prediction accuracy. Our goal is to create a model that, using environmental variables like elevation, rainfall, temperature, and soil type, can precisely forecast the number of trees in each location. To train and evaluate our model, we employ a dataset of more than 500,000 forested regions located throughout the United States. With predictions of forest tree cover being upto 97% accurate, the results demonstrate that the Random Forest model outperforms conventional machine learning techniques. Machine learning may be utilised as a useful tool in environmental management and conservation efforts by estimating the amount of forest tree cover. Accurate tree cover forecast can help in determining which regions need forest preservation, reforestation, or both.