Machine Learning For Environmental Monitoring

Abstract

This thesis discusses machine learning transformations in environmental contexts using a dataset that contains information about both historical and current environments. The dataset, which spans the years 2013 to 2022, includes about 10 different locales. Machine learning algorithms can execute a change suggestion and a synopsis of the climate technological strategy. The suggested machine learning approach, using the Random Forest Classifier, shows an accuracy of 83.5%. Build a temporal framework, include historical data, and create models that forecast seasonal and long-term trends. Predictive modeling of ecological processes to plan a fragmentation is supported by the integration of climate data and historical background. Using visual aids and intuition to traverse the intricacies of environmental conditions is the foundation of this multidisciplinary endeavor. To decode complicated data and forecast environmental change, our world requires machine learning.