Predictive Analysis for Environmental Risk Assessment in Coastal Regions

Coastal regions are especially vulnerable to environmental risks due to factors such as rising sea levels, extreme weather conditions, and human activities. Assessing these risks in real-time requires an integration of predictive analyt-ics and advanced models that consider both environmental and anthropogenic variables. This paper presents a comprehensive predictive analysis framework designed for environmental risk assessment in coastal regions. The proposed framework leverages real-time data collected from various sources, including meteorological stations and oceanographic sensors, to model potential haz-ards and assess the likelihood of risk events. The predictive analysis frame-work consists of two models: one based on traditional statistical techniques and another utilizing machine learning approaches. These models are com-pared in terms of accuracy, efficiency, and computational performance. The methodology incorporates real-time sensor data and historical records to sim-ulate potential risk scenarios and evaluate their impact on coastal infrastruc-ture and ecosystems. A comparative analysis is performed on multiple da-tasets to validate the performance of the two models, providing insights into which model performs better under varying environmental conditions. The results indicate that machine learning-based models outperform traditional statistical models in terms of predictive accuracy and computational efficiency. This paper concludes with a discussion of the implications of these findings for environmental management and policy formulation in coastal regions. The results underscore the importance of integrating advanced predictive models with real-time data to mitigate environmental risks and ensure the sustainability of coastal areas