Exploring machine learning in the context of environmental usage prediction

Abstract

The use of environmental prediction refers to predicting the impact that human activity will have on ecosystems, natural resources and other environmental factors in the future. This strategy looks at historical patterns, present situations and future predictions to hypothesize about the ecological effects of human activities, climate change and other factors. This research suggests machine learning(ML) techniques to predict environmental uses. Prediction accuracy declinesover time and models face challenges due to the need for observable data integration in sectors like agriculture, energy and waterfor successful sub-seasonal predictions. To tackle these issues, we proposed a Next Generation Bumble Bee Mating Optimized Naïve Bayes Algorithm (NGBBMO-NBA) method that is used to enhance weather prediction. The research gathers the SSF dataset to make predictions on the usage of the environment. We use a min-max normalization approach for data preprocessing. The principalcomponent analysis (PCA) method extracts features from the SSF data. Environmental uncertainty inhibits sub-seasonal projections. Our suggested method, NGBBMO-NBA, surpasses the current techniques for ecological prediction in terms of energy consumption (96.5%), F1-Score (96%), Mean Absolute Error (MAE) (97) and Root Mean Square Error (RMSE) (98.5).