Environmental Risk Assessment Models Using Data-Driven Approaches

Veronika Elekné Fodor

Environmental Risk Assessment Models Using Data-Driven Approaches

Author(s)	Dr. Veronika Elekné Fodor
Country	United States
Abstract	Increasing environmental uncertainties, climate-related hazards, ecological degradation, and complex interactions between natural and human systems have created a strong need for advanced environmental risk assessment models. Traditional risk assessment approaches often depend on historical observations, predefined indicators, and conventional analytical methods that may have limitations in processing large-scale environmental datasets and rapidly changing ecological conditions. Data-driven approaches have emerged as advanced frameworks capable of improving risk prediction, environmental monitoring, and evidence-based decision-making. This study examines environmental risk assessment models using data-driven approaches by analysing their contribution to risk prediction accuracy, environmental monitoring capability, uncertainty reduction, and decision-support effectiveness. The research explores key data-driven technologies including machine learning algorithms, predictive analytics, remote sensing analysis, geographic information systems, artificial intelligence models, and environmental data processing frameworks. A simulation-based analytical methodology is developed to compare conventional risk assessment methods with data-driven environmental risk models using evaluation indicators including prediction accuracy, risk detection capability, monitoring efficiency, and management effectiveness. The findings demonstrate that data-driven risk assessment models significantly improve environmental risk identification and management through advanced data analysis, predictive modelling, and intelligent pattern recognition. The results indicate that integrating environmental datasets with analytical technologies provides stronger capabilities for detecting potential hazards and supporting proactive environmental planning. However, successful implementation requires addressing challenges related to data quality, model reliability, computational requirements, infrastructure development, and practical decision integration.
Keywords	Environmental Risk Assessment, Data-Driven Approaches, Machine Learning, Predictive Analytics, Environmental Monitoring, Risk Prediction, Decision Support Systems
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-22

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American International Journal of Research and Innovation

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Environmental Risk Assessment Models Using Data-Driven Approaches

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