Efficient prediction in forest fire alert system using logistic regression and a novel tree specific random forest classifiers.

The accuracy and precision with which Logistic Regression (LR) and Random Forest can predict forest fires are compared in this study (RF). The List Consists Of The Random Forest method outperforms other machine learning techniques in fire detection speed and accuracy. To better detect forest fires, a system that compares Random Forest with Logistic Regression was developed. Using G power, we determined that 28 people were needed for each experiment. The sample size for the pilot study was calculated to be 95 percent confident. The dataset shows that the Random Forest (RF) model is 95 percent accurate in predicting forest fires, whereas the Logistic Regression (LR) model is 62 percent accurate. The p-value for Logistic Regression is 0.005, but Random Forest is a superior classifier. In terms of accuracy and precision, Random Forest is superior to Logistic Regression. [ABSTRACT FROM AUTHOR]