Earthquake Damage Assessment Based on User Generated Data in Social Networks
Abstract
:1. Introduction
2. Literature Review
3. Materials and Methods
3.1. Data and Case Study
3.2. Methodology
3.2.1. Data Preprocessing
- Tokenization
- Transform cases
- Removing stop words
- Removing short words
- Stemming
3.2.2. Classification
3.2.3. Assessment Performance of Classification
3.2.4. Damage Assessment (Damage Map Creation)
3.2.5. Validation
- True Positives (TP): These are cases in which we predicted yes, and they were actually yes.
- True Negatives (TN): We predicted no, and they were actually no.
- False Positives (FP): We predicted yes, but they were actually no.
- False Negatives (FN): We predicted no, but they were actually yes.
4. Results
4.1. Classification
4.2. Damage Assessment (Damage Map Creation)
4.3. Damage Map Validation
5. Conclusions and Suggestions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Accuracy | Precision | Classification Error | Recall | F-Score |
---|---|---|---|---|
71.03% | 56.80% | 28.97% | 59.25% | 58% |
2014 Napa Earthquake | |
---|---|
Tweet Text | Damage Class Label |
#PLEASE pray for all first responders that will assist with fires and floods today due to earthquake damage. | Damage |
Napa earthquake: Residents, many of them seniors, flee from devastating mobile home fire: An intense blaze burned. | Damage |
Earthquake damage in Napa. | Damage |
Hope everyone in and around Solano county is fine! Time to get back to sleep here… #earthquake | Non-damage |
Too drunk to have felt any earthquake #tbh | Non-damage |
@starsandrobots @earthquakesSF though it looks like quakes in the 1–2 scale happen every day… | Non-damage |
Overall Accuracy | Precision | Recall | F-Score |
---|---|---|---|
69.89% | 48.36% | 58.98% | 53.14% |
County Name | The Official Intensity Map Damage Ranking | Our Approach Damage Ranking |
---|---|---|
Napa | 1 | 8 |
Solano | 2 | 3 |
Sonoma | 3 | 1 |
San Francisco | 4 | 12 |
Contra Costa | 5 | 4 |
Marin | 6 | 14 |
Sacramento | 7 | 10 |
San Mateo | 8 | 6 |
Alameda | 9 | 2 |
Yoko | 10 | 9 |
Lake | 11 | 7 |
San Joaquin | 12 | 11 |
Placer | 13 | 13 |
El Dorado | 14 | 16 |
Stanislaus | 15 | 15 |
Santa Clara | 16 | 5 |
Spearman’s Rho | Pearson Correlation |
---|---|
0.429 | 0.503 |
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Ahadzadeh, S.; Malek, M.R. Earthquake Damage Assessment Based on User Generated Data in Social Networks. Sustainability 2021, 13, 4814. https://doi.org/10.3390/su13094814
Ahadzadeh S, Malek MR. Earthquake Damage Assessment Based on User Generated Data in Social Networks. Sustainability. 2021; 13(9):4814. https://doi.org/10.3390/su13094814
Chicago/Turabian StyleAhadzadeh, Sajjad, and Mohammad Reza Malek. 2021. "Earthquake Damage Assessment Based on User Generated Data in Social Networks" Sustainability 13, no. 9: 4814. https://doi.org/10.3390/su13094814