Optimization of Random Forest Model with Correlation-Based Feature Selection for Enhanced Forest Health Prediction
Abstract
Forest health serves as a key indicator for maintaining ecosystem sustainability and biodiversity. This study aims to predict forest health status using a Random Forest algorithm integrated with Correlation-Based Feature Selection (CFS). The dataset comprises 1,000 samples with 18 attributes—including Disturbance_Level, Fire_Risk_Index, Tree_Height, and Menhinick_Index—along with health status labels categorized into four classes: Unhealthy, Sub-Healthy, Healthy, and Very Healthy. The research methodology encompassed data preprocessing, feature selection using CFS, Random Forest model construction, and performance evaluation. Feature selection identified four key attributes that significantly contributed to forest health prediction. The model was trained on 70% of the data and tested on the remaining 30%, achieving an accuracy of 92%. Further analysis revealed an average precision of 91%, recall of 90%, and F1-score of 90%. The confusion matrix indicated accurate predictions across most categories, though some misclassification occurred in the Sub-Healthy class. This study demonstrates that the CFS-based Random Forest approach is effective for forest health prediction, offering a valuable analytical tool to support conservation efforts and damage risk mitigation.
, , , Predictive Modeling
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