Design of an Enhanced Environmental Monitoring Model through Machine Learning Integration for Sustainable Soil and Water Management

Abstract

In the quest for sustainable growth and environmental conservation, there is a growing need to address the challenges associated with the health of soil and water areas. Traditional methods often fall short in providing timely and accurate predictions essential for effective environmental management. To bridge this gap, a novel machine learning-based process, integrating Predictive Environmental Analytics (PEA), Satellite-Integrated Environmental Synthesis (SIES), Automated Eco-Sample Analyzer (AESA), and Dynamic Environmental Learning Framework (DELF), has been developed and tested in the Indore and Pune regions. Existing environmental monitoring techniques often lack the precision and adaptability required to effectively anticipate and respond to rapid ecological changes. Their limitations include inadequate spatial coverage, delayed data processing, and static models that fail to evolve with environmental dynamics. This research addresses these shortcomings by harnessing the power of machine learning in predictive environmental analysis. The proposed model employs PEA for advanced predictive capabilities using historical and real-time data, enabling accurate forecasts of soil and water health. SIES leverages remote sensing data for comprehensive environmental change monitoring over vast areas. AESA enhances the efficiency and accuracy of environmental sample analysis, providing rapid assessments of key health indicators. Lastly, DELF offers an adaptive learning system that continually evolves with new environmental data, ensuring long-term relevance and accuracy of predictive models. The application of this integrated approach in Indore and Pune has demonstrated significant improvements over existing methods, including 8.5% higher precision, 5.5% higher accuracy, 8.3% higher recall, 4.9% higher AUC, 2.5% higher specificity in Sustainable Development Goals (SDG) continuous learning, and a 4.9% reduction in delay. These results underscore the potential of this novel approach in enhancing environmental monitoring and decision-making processes. The integration of machine learning with environmental data analytics presents a promising avenue for achieving continuous sustainable growth and proactive environmental analysis.