Climate Events Impact Prediction and Economics Resilience Analysis

Abstract

Climate change has significantly increased the occurrence of extreme climate events such as floods, droughts, hurricanes, and heatwaves. These disasters cause severe economic damage, affecting agriculture, infrastructure, industries, and human livelihoods. This research proposes a Climate Impact Prediction and Economic Resilience Analytics System that integrates climate data, machine learning models, and economic indicators to predict disaster risks and analyze economic recovery capabilities. The proposed system collects climate data such as temperature, rainfall, humidity, and wind speed along with economic indicators like GDP growth, agricultural productivity, and infrastructure damage. Using data analytics and machine learning techniques, the system identifies climate risk patterns and predicts future climate disasters. The results help policymakers understand vulnerable regions, estimate economic losses, and design strategies for improving economic resilience. This approach enables governments and organizations to make informed decisions for disaster preparedness, climate adaptation, and sustainable economic development.

Introduction

Climate change intensifies extreme weather events—hurricanes, floods, droughts, wildfires, and heatwaves—causing significant economic losses in agriculture, infrastructure, and overall financial stability. Traditional disaster management focuses on post-event recovery, lacking predictive capabilities to prevent economic damage.

The research proposes ClimateResNet, a hybrid deep learning system integrating climate monitoring, machine learning, and economic analysis to predict the financial impact of extreme climate events. The framework collects and preprocesses meteorological, satellite, and socio-economic data, extracts climate and economic features, and uses models like Random Forest, SVM, LSTM, and hybrid deep learning for predictive modeling.

The system evaluates economic consequences, including agricultural loss, infrastructure damage, and GDP changes, providing resilience indicators for recovery planning. Experimental results show ClimateResNet outperforms traditional models, achieving the highest accuracy (R² = 0.95) in capturing spatial climate variations and temporal economic trends, enabling proactive disaster mitigation and economic resilience planning.

Conclusion

This research presents an intelligent system for climate event impact prediction and economic resilience analysis. The system integrates climate data analytics with machine learning models to forecast climate disasters and evaluate their economic consequences. By combining climate monitoring systems with economic resilience metrics, the proposed framework enables governments and organizations to develop proactive disaster management strategies. The predictive model demonstrates high accuracy and provides valuable insights into the relationship between climate variability and economic stability. The research highlights the importance of data-driven climate risk assessment in building resilient economies and sustainable infrastructure systems.

References

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Copyright

Copyright © 2026 Ms. Kokila, Kodidhi Sharadha, Kudum Aravind, Lingadahal Shasikumar, MC Meghasree, Madiri Jaya Prakash. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.