Incorporating Machine Learning into Environmental Impact Assessments for Sustainable Development - Varikunta, Obulesu; Reddy, A. Sarveswara; Sathish, K. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The growing concerns surrounding environmental degradation and the imperative for sustainable development have brought about a significant paradigm shift in the methodologies employed in Environmental Impact Assessments (EIAs). This research paper investigates the application of Machine Learning (ML) methodologies to Environmental Impact Assessments (EIAs) to improve their precision, productivity, and overall efficacy in the pursuit of sustainable development. By conducting an extensive review of pertinent scholarly works, case studies, and emergent patterns, the objective of this paper is to clarify the possible advantages and obstacles that may arise from the integration of machine learning into the EIA procedure. The subtopics that have been identified encompass the preprocessing of data predictive modeling, decision support systems, and the ethical implications that arise from the convergence of technology and environmental preservation. In conclusion, this study proposes that environmental science and state-of-the-art ML methodologies work in tandem to foster a more sustainable and resilient future through harmonious collaboration.