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Disaster-based Financial Decision Framework Using Machine Learning in India
Implementing Partner: Build Change
Project name:Disaster-based financial decision framework using Machine Learning in India
This project enhances disaster damage assessments by replacing manual methods with advanced automation, streamlining efforts and conserving resources.
By delivering real-time damage updates, it accelerates recovery planning and supports informed financial decisions for disaster response. Improved efficiency ensures swift, data-driven actions, optimizing resource allocation.
Ultimately, it transforms disaster management by enabling proactive strategies, reducing delays, and enhancing overall effectiveness in responding to crises.
floods in India
caused to buildings
Impact and Key Findings
1
A vast dataset of labeled disaster images trained multiple models, enhancing their ability to detect diverse damage patterns. This rigorous training significantly improves assessment accuracy, enabling precise evaluations across various real-world disaster scenarios for effective response and recovery.
2
Advanced deep learning techniques enabled the model to analyze complex disaster scenarios with precision. During testing, it consistently identified and categorized damage with high reliability, enhancing assessment accuracy and supporting swift, data-driven disaster response and recovery efforts.
3
The study developed a rapid, automated disaster damage assessment method, drastically reducing manual effort and accelerating evaluations. This approach ensures timely, precise information, enabling more effective disaster response and recovery planning while optimizing resource allocation for impacted areas.
