AI-POWERED INSIGHTS
Assessment of university students' earthquake coping strategies using artificial intelligence methods
Earthquakes are one of the most destructive natural disasters that pose a serious threat to human life and infrastructure worldwide. The aim of this study is to evaluate the coping strategies of adult individuals in Turkey regarding earthquake stress using artificial intelligence-based methods. The data was collected from 858 university students living in Turkey during January, February, and March 2024. A dataset was created using the 'Coping Scale for Earthquake Stress.' Prediction models were established using artificial intelligence algorithms such as Logistic Regression (LR), Bagging, and Random Forest (RF) based on information from 24 variables. The cross-validation method was applied during model training. The Logistic Regression algorithm achieved the highest accuracy rate of 98.60%, while the Bagging algorithm demonstrated the lowest performance with an accuracy rate of 79.95%. The Random Forest algorithm showed moderate performance with an accuracy rate of 85.89%. The findings provide important insights into the coping strategies of the community regarding earthquake stress. This study is expected to contribute significantly to areas such as disaster management, psychology, public health, and community resilience.
Executive Impact at a Glance
Leveraging advanced AI, this research delivers critical insights into human resilience during natural disasters, setting new benchmarks for predictive accuracy in psychological assessment.
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LR Model Accuracy
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University Students Surveyed
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Key Variables Analyzed
Deep Analysis & Enterprise Applications
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98.60%
Peak Prediction Accuracy (Logistic Regression)
Identifying Core Coping Factors
Using the Random Forest algorithm, the study identified the most influential variables in earthquake coping strategies. Key factors included CESS_13 ('I try to be more optimistic about life') with an importance score of 0.086, CESS_10 ('I believe in fate and that it cannot be changed') at 0.084, and CESS_14 ('I try to think positively') at 0.082.
Other significant variables for predicting coping strategies were CESS_9 ('I try to find comfort through prayer') at 0.075, CESS_8 ('I think that death is inevitable') at 0.062, and CESS_2 ('I entrust myself to God') at 0.056. These highlight the importance of faith and existential perspectives in coping mechanisms.
Demographic and structural characteristics like Age (0.035), Year_of_Construction (0.026), Living_Place (0.025), and Building_Floor (0.025) showed moderate influence. Interestingly, personal factors such as Gender (0.009) and prior earthquake experience (0.009) had the lowest impact on predictive performance, indicating that coping strategies are more closely tied to attitudinal variables rather than basic demographics.
Enterprise Process Flow
Coping with Earthquake Stress Strategy Dataset
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Dataset Split Method Cross Validation
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Application of Machine Learning Algorithms
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Performance Evaluation
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Calculation of Confusion Matrix, Performance Metrics, Correlation Matrix Values
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Choosing the Best Algorithm
| Algorithm | Accuracy (%) | Precision (%) | Recall (%) | F1-Score | MCC |
|---|---|---|---|---|---|
| Logistic Regression (LR) | 98.60 | 98.60 | 98.60 | 98.60 | 97.90 |
| Random Forest (RF) | 85.89 | 86.80 | 85.90 | 85.50 | 76.70 |
| Bagging | 79.95 | 80.80 | 80.00 | 79.50 | 66.36 |
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