Скачать или смотреть Machine Learning for Environmental Protection - Optimized Emissions and Optimized Costs Using Python

Machine learning for environmental protection aims to optimize emissions and costs while enhancing sustainability and efficiency. It involves using algorithms to model relationships between city characteristics, such as population, vehicle count, and energy usage, and their environmental impact. In this case study, machine learning models predict emissions based on various city attributes, allowing for the analysis of factors like population density, industrial activity, and renewable energy usage. Using a random forest regressor, the study generates synthetic data and evaluates the model's accuracy with metrics like mean absolute error and R-squared. The study also performs scenario and simulation analyses to predict the effects of policy changes on emissions, including Monte Carlo simulations for emission reductions across different city sizes. Optimization techniques are employed to minimize transportation and energy emissions while maximizing renewable energy usage, resulting in a reduction in emissions and costs. The model identifies the potential for cost savings and emissions reductions by adjusting energy consumption and transportation systems. The results show substantial reductions in transportation and energy emissions, alongside increased renewable energy usage. The optimization process benefits from minimizing emissions and costs while promoting cleaner energy solutions and efficient systems. This approach provides valuable insights for urban planners, businesses, and governments to make sustainable decisions and improve environmental outcomes.