Assessment of the seismic vulnerability in an urban area with the integration of machine learning methods and GIS

Ayhan Doğan, Murat Başeğmez and Cevdet Coşkun Aydın ()
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Ayhan Doğan: Hacettepe University
Murat Başeğmez: Ministry of National Education
Cevdet Coşkun Aydın: Hacettepe University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 8, No 26, 9613-9652

Abstract: Abstract Predicting earthquake risk areas and risk levels is vital in minimizing the loss of life. In this study, earthquake risk assessment has been conducted by producing predictions for both five-class and two-class risk levels. The methods were tested on Izmir province. For this purpose, the city was divided into 28 zones. Twenty-two different evaluation criteria were assessed using geographic information systems. Risky areas were predicted using Support Vector Machines, k-Nearest Neighbors, Naive Bayes, Decision Trees, and Ensemble classifiers. It has been concluded that the F1 score results, the highest prediction success in training is ensemble classifier with 96%, and tests is decision tree methods with 45% for five classes. In addition, the training results is the ensemble classifier with 98%, and the test results is the decision tree methods with 76% for two classes. When all machine learning results were examined together, test prediction success on data labeled with two-classes was found to be significantly more successful than on data labeled with five classes. As a result of this study, it has been observed that Multi-Criteria Decision Making and machine learning give significant results in the area-based earthquake vulnerability analysis performed together. In addition, this study provides a practical contribution to urban planning and the improvement of development strategies in İzmir by identifying high-risk areas to mitigate seismic risks. Furthermore, the findings offer a data-driven framework for enhancing disaster management policies, enabling authorities to effectively plan emergency responses in vulnerable regions, implement appropriate construction techniques in high-risk areas, and optimize resource allocation.

Keywords: Earthquake vulnerability; Machine learning; GIS; MCDM (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07185-4

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