Utilizing Data Mining Techniques in Geophysical and Biological Analysis for Assessing Environmental Risks

Abstract

This study aimed to assess environmental risks using data extraction techniques. It focused on geophysical and biological factors and addressed the urgent need for effective risk management strategies to reduce soil erosion, water pollution, and air quality deterioration. A comprehensive dataset was created through the systematic collection of geophysical and biological data including temperature, soil composition, and biological abundance index. It used equipment such as satellite sensors and mountain transmitting stations. Various statistical tools used include decision trees and random forest algorithms. They were used to analyze data and identify important environmental risk factors. The results showed some interesting insights, revealing that the Neural Network has an accuracy of 95.5%, and the Random Forest algorithm predicts risk with an accuracy of 92.0%. It analyzed the classification of environmental hazard zones and identified high-risk zones, such as Zone A, which contains 10,000 people affected by erosion and Zone D, 20,000 people who were at risk from soil contamination. The study concludes that social media can significantly improve understanding and management of environmental risks. Additionally, it provides a useful framework for decision-makers and stakeholders to promote sustainable environmental practices.