Year: 2024
Type: article
Abstract: 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
Author: Deepanshi Aggarwal
Language: English
Topic: Coastal and Marine Dynamics
Subfield: Earth-Surface Processes
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408908214