Today, one of the main concerns that the world faces is climate destruction.
The hazardous gases emitted by material waste are a critical component among many
that contribute to the constantly changing atmospheric conditions. Waste from
agriculture is also included in this group. Controlling the production of these gases
largely depends on accurately predicting the amount to be produced. The current study
offers new classifiers and prediction models based on machine learning to forecast the
generation of hazardous gases from agricultural waste. The Random Forest method
achieved 100% classification accuracy after feature optimization and selection were
applied to the gathered dataset. Logistic regression, AdaBoost, and Decision Tree
ranked second, third, and fourth, respectively, with classification accuracies of 97.19%,
97.18%, and 97.14%. SVM provided the highest prediction accuracy, 98%, in
regression models.
Keywords: Artificial intelligence, Environment, Green energy, Machine Learning, Prediction, Renewable energy.
