Urbanization enables economic growth but also harms the environment through
degradation. Traditional methods of detecting environmental issues have proven
inefficient. Machine learning has emerged as a promising tool for tracking
environmental deterioration by identifying key predictive features. Recent
research focused on developing a predictive model using pollutant levels and
particulate matter as indicators of environmental state in order to outline
challenges. Machine learning was employed to identify patterns linking areas
with worse conditions. This research aims to assist governments in identifying
intervention points, improving planning and conservation efforts, and
ultimately contributing to sustainable development.

城市化对经济增长有促进作用，但也通过环境恶化对环境造成危害。机器学习已成为一个有很大潜力的工具，通过识别关键的预测特征来跟踪环境恶化。最近的研究专注于利用污染物水平和颗粒物作为环境状态指标来开发预测模型，以勾勒出所面临的挑战。机器学习被用来识别与环境恶化相关的模式。该研究旨在帮助政府识别干预点，改进规划和保护工作，并最终促进可持续发展。