Towards Sustainable Development: A Novel Integrated Machine Learning Model for Holistic Environmental Health Monitoring

TL;DR

This paper introduces a novel integrated machine learning model designed for comprehensive environmental health monitoring to support sustainable urban development and effective environmental management.

Contribution

It presents a new holistic machine learning approach that combines multiple environmental indicators for improved detection and prediction of environmental degradation.

Findings

Effective identification of pollution hotspots

Enhanced prediction accuracy of environmental deterioration

Supports policy-making for sustainable urban planning

Abstract

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.