Machine Learning and Data Analysis for Environmental Management

Data upon air contaminants, climate change, especially the general population have expanded dramatically. While big datasets had a lot of potential, they also pose challenges to conventional epidemic methods. As a result, there is a growing inclination to look at other techniques including collecting data, identification of patterns, and forecasting. In solution to these challenges, data collection and predictive modeling approaches are being progressively included to the research environment of air pollution epidemiological. A comprehensive review of the scientific literature was conducted by exploring on Professor, Scholar, the Embl database structure, and Nih for study articles in the field for ozone in the air disease utilizing data mining and computational methods for learning. The assessment aimed to highlight the most recent developments, persistent challenges, and fresh avenues for investigation into the use of techniques for data mining in the study of air pollution. The investigation's findings demonstrate the expanding use medical methods for data mining in the field of epidemiological pertaining to air contamination. Continuous developments in artificial intelligence and data extractive industries, especially in spatiotemporal and geographic mining, are making it easier to improve air pollution epidemiological. New sensors and storage devices that can give more as well as more reliable information might make apps in the future considerably more fruitful.