Environmental Impact Prediction

Predicting environmental impacts is crucial for evaluating and reducing how human activity affects ecosystems, particularly when it comes to pollution, resource depletion, and climate change. In order to predict changes in the environment, this study looks at a variety of predictive models, including both sophisticated machine learning techniques and conventional statistical methods. We provide an integrated framework that increases prediction accuracy by utilizing data from several sources, including satellite images, climate records, air and water quality indexes, and socioeconomic characteristics. Using machine learning models, the study shows improved performance in short-term predictions of water pollution, soil deterioration, and air quality. However, due to data limitations and the complexity of ecological systems, long-term, complicated predictions—like those regarding habitat degradation and biodiversity loss—remain difficult. According to our research, enhancing future environmental effect projections and facilitating more sustainable decision-making processes would need incorporating real-time data, growing datasets, and creating adaptive models.