Integrating GIS and Machine Learning for Seismic Damage Assessment of Liquid Storage Tanks in California
Bibliographic record
Abstract
This study explores the integration of Geographic Information Systems (GIS) and Machine Learning (ML) methods to assess seismic-induced damage categories of above-ground liquid storage tanks across California.A real-world damage tank dataset was considered to perform GIS-based spatial analysis, to identify clustering and distribution patterns.The results reveal that Southern California's most significant damage concentrations align with the region's high seismic vulnerability.To predict damage categories, four ML classifiers were evaluated: Decision Tree (DT), Random Forest (RF), XGBoost, and Support Vector Machine (SVM).Initial model performance was limited due to class imbalance in the dataset.The Random Forest model showed relatively better results compared to the others and was further improved using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance.The enhanced model significantly improved classification performance, achieving training scores of 0.93 across all evaluation metrics.On the test set, the model attained a maximum precision of 0.75, a recall of 0.73, and an accuracy of 0.73.These findings demonstrate that combining Random Forest with SMOTE can effectively improve predictive accuracy and generalization in imbalanced datasets.Overall, this research highlights the practical application of GIS-based spatial analysis with ML techniques for seismic risk assessment and infrastructure resilience planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".