An AI-based framework for earthquake relief demand forecasting: A case study in Türkiye

• Published in: International journal of disaster risk reduction 2024-02, Vol.102, p.104287, Article 104287
• Main Authors: Biswas, Saptadeep, Kumar, Dhruv, Hajiaghaei-Keshteli, Mostafa, Bera, Uttam Kumar
• Format: Article
• Language: English
• Subjects: AI-based framework; Disaster management; Earthquake emergency response; Humanitarian logistics; Machine learning feedback loop; Predictive modeling; Relief demand forecasting; Vulnerability assessment
• ISSN: 2212-4209; 2212-4209
• DOI: 10.1016/j.ijdrr.2024.104287

Accurately forecasting the demand for relief assistance is crucial for efficient post-disaster relief operations. This paper presents an AI-based framework for earthquake relief demand forecasting, aiming to optimize the distribution of emergency resources. To identify higher-risk areas, the framework integrates historical earthquake data with socioeconomic information. This information aids in identifying regions that require immediate relief assistance. The initial dataset for relief demand quantification includes essential parameters such as Latitude, Longitude, Population, Demand, Supply, Cost, Deaths, Injuries, Infrastructural Damage, and Cascading Disaster. By analyzing these inputs and factors, AI algorithms can quantify the relief demand scenario accurately, resulting in accurate and reliable forecasts of relief supply requirements. The iterative ML techniques in generating comprehensive data for Türkiye greatly enhance the quality and accuracy of relief demand prediction. The prediction models used in the framework, namely Least Angle Regression and Linear Regression, demonstrate exceptional performance in forecasting relief demand. They achieve a remarkable R2 score of 1, indicating a perfect fit to the data, and a Loss Error function value of zero, signifying highly accurate predictions. Additionally, the Decision Tree algorithm showcases strong capabilities in iterative learning, achieving an accuracy rate of 88.99 %. In addition to providing valuable insights for tailored relief strategies, the framework extends beyond relief demand by predicting outcomes such as casualties, injuries, and infrastructure damage. With a comprehensive understanding of the earthquake’s impact, relief organizations can strategize their response and allocate resources accordingly. The thorough methodology and innovative application of ML algorithms within our pioneering AI-based framework significantly contribute to the field. This signifies a fundamental shift in the approach to envisioning and executing relief operations following seismic events. [Display omitted]