AI for Disaster Risk Management: Integrating Traditional Knowledge of Natural Disaster Prediction

Abstract

Natural disasters, ranging from earthquakes to floods, pose significant challenges to vulnerable regions worldwide. Traditional knowledge systems, often deeply rooted in local cultures, have historically provided valuable insights into predicting such events. In the modern era, Artificial Intelligence (AI) is emerging as a powerful tool in disaster risk management, offering potential solutions for early warning systems, resource allocation, and post-disaster recovery. This paper investigates the integration of traditional knowledge with AI for disaster risk management, focusing on the development of predictive models, leveraging data sources such as meteorological records, satellite imagery, and indigenous wisdom. By combining machine learning algorithms with time-tested traditional techniques, we explore how AI can enhance disaster preparedness, resilience, and mitigation efforts in disaster-prone areas. This study highlights the importance of a hybrid approach, which combines scientific advancements with cultural practices, to create a more effective disaster response system.

Introduction

Natural disasters cause severe damage and socioeconomic impacts worldwide, especially in developing, disaster-prone regions where effective disaster risk management (DRM) is critical. While modern DRM relies on scientific data and technology, traditional knowledge—passed down through generations via stories, rituals, and observations of nature—also holds valuable insights into predicting natural events.

Recent advances in artificial intelligence (AI), particularly machine learning (ML), enable processing large, diverse data sources (satellite imagery, sensors, weather reports, social media) to forecast disasters and optimize relief efforts. Combining AI with traditional knowledge offers a promising approach to develop culturally sensitive, accurate, and robust disaster management systems.

Related Work:
Studies show AI methods (neural networks, SVM, decision trees) predict events like earthquakes, floods, and cyclones but often overlook local traditional knowledge, which can improve predictions by incorporating unique environmental indicators such as animal behavior and plant patterns. Although traditional methods are sometimes viewed as subjective, integrating them with AI is increasingly recognized as valuable.

Methodology:
This research uses a mixed-methods approach—collecting traditional knowledge through interviews and ethnographic studies, and modern disaster data from meteorological and satellite sources. Machine learning models (Random Forest, SVM, Neural Networks) are trained on historical data and enhanced by features derived from indigenous knowledge. Purposive sampling focuses on disaster-prone regions in India to capture diverse prediction practices.

Results and Discussion:
Integrating traditional knowledge with AI models improved prediction accuracy, especially where modern data coverage is limited. Indigenous indicators like animal migrations and water body changes enhanced model performance. Challenges include the oral, unstructured nature of traditional knowledge and cultural sensitivities around data sharing. The study advocates a hybrid DRM approach, where AI validates and enriches traditional wisdom, leading to better preparedness and response.

Conclusion

The integration of Artificial Intelligence (AI) with traditional knowledge represents a promising avenue for enhancing disaster risk management in areas vulnerable to natural disasters. By leveraging machine learning techniques alongside indigenous wisdom, it is possible to develop prediction models that are both accurate and culturally appropriate, tailored to the specific needs of affected communities. This hybrid approach not only allows for more effective early warning systems but also fosters a deeper understanding of local risk factors. However, the integration process faces several challenges, including data availability, interoperability, and maintaining the authenticity of indigenous knowledge. Despite these hurdles, this convergence of technology and tradition offers significant potential for improving disaster preparedness, response, and recovery. By prioritizing interdisciplinary research and collaboration, this approach can contribute to building more resilient communities that are better equipped to face natural disasters in the future.

References

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Copyright

Copyright © 2025 Himanshu Kashyap. 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.