Environmental Risk Assessment Models Using Advanced Analytics

Ndeke Musee

Environmental Risk Assessment Models Using Advanced Analytics

Author(s)	Prof. Ndeke Musee
Country	South Africa
Abstract	Environmental risks associated with climate change, industrial activities, urbanization, resource exploitation, and ecological degradation have increased the need for reliable risk assessment approaches. Conventional environmental assessment methods can be constrained by limited datasets, uncertain environmental interactions, delayed monitoring, and difficulties in processing complex information. Advanced analytics provides opportunities to strengthen environmental risk assessment through artificial intelligence, machine learning, geographic information systems, remote sensing, Internet of Things technologies, predictive modeling, and large-scale environmental data analysis. This study examines environmental risk assessment models using advanced analytics, focusing on environmental monitoring, predictive risk modeling, spatial analysis, climate-related risk assessment, pollution detection, ecological vulnerability, and data-driven decision-making. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The study analyzes how advanced analytical approaches can integrate environmental, climatic, geographic, and socioeconomic information to identify potential risks and support timely intervention. Machine learning models can identify complex relationships within environmental datasets, while remote sensing and GIS technologies can support spatial risk mapping and monitoring. Advanced analytics can also improve early-warning systems by identifying patterns associated with pollution events, extreme weather, ecosystem degradation, and environmental hazards. Nevertheless, challenges include data quality, uncertainty, model interpretability, technological costs, interoperability, privacy concerns, and shortages of specialized analytical expertise. The study emphasizes integrated environmental risk frameworks combining advanced analytics with continuous monitoring, spatial intelligence, predictive modeling, and evidence-based environmental management. Future research should investigate explainable artificial intelligence, digital twins, real-time environmental intelligence, multimodal environmental data integration, climate-risk forecasting, and automated early-warning systems. Such developments can strengthen environmental decision-making and support more proactive approaches to environmental protection and risk reduction.
Keywords	Environmental Risk Assessment, Advanced Analytics, Artificial Intelligence, Machine Learning, Environmental Monitoring, Predictive Modeling, Geographic Information Systems, Climate Risk, Remote Sensing, Environmental Management.
Field	Engineering
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-28

View / Download PDF File

About AIJARD Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijard.com

American International Journal of Advanced Research and Development

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Environmental Risk Assessment Models Using Advanced Analytics

Share this