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<p>Accurately predicting groundwater level (GWL) changes induced by seismic events remains a key challenge in hydrogeological research, with significant implications for earthquake preparedness and water resource management. This study proposes a novel machine learning framework that combines automated feature engineering with multi-model classification to forecast persistent GWL changes following earthquakes. Using a well-curated dataset of 2,563 GWL responses from 495 wells in New Zealand, we employ the AutoFeat library to construct a set of engineered features that capture complex interactions between seismic parameters and aquifer characteristics. A range of classifiers, including Gradient Boosting, Random Forest, Logistic Regression, and others, were benchmarked using the PyCaret library, with model performance evaluated through accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Gradient Boosting emerged as the top-performing model, especially after hyperparameter optimization, achieving an AUC of 0.89 and F1-score of 0.62. Feature importance analysis further demonstrated the superior predictive value of engineered variables over traditional seismic metrics. The results validate the effectiveness of combining feature construction and ensemble learning for improved earthquake-induced GWL prediction and offer a scalable, interpretable approach for future seismic-hydrological modeling applications.</p>

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Enhanced prediction of persistent earthquake-induced groundwater level changes with advanced feature engineering and machine learning

  • 期刊论文

  • 发表日期：

    2025年10月17日

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  • Amirhossein Tahmouresi,
  • Sogand Basirian,
  • Amirhossein Javanshir,
  • YoungJin Cha

摘要

Accurately predicting groundwater level (GWL) changes induced by seismic events remains a key challenge in hydrogeological research, with significant implications for earthquake preparedness and water resource management. This study proposes a novel machine learning framework that combines automated feature engineering with multi-model classification to forecast persistent GWL changes following earthquakes. Using a well-curated dataset of 2,563 GWL responses from 495 wells in New Zealand, we employ the AutoFeat library to construct a set of engineered features that capture complex interactions between seismic parameters and aquifer characteristics. A range of classifiers, including Gradient Boosting, Random Forest, Logistic Regression, and others, were benchmarked using the PyCaret library, with model performance evaluated through accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Gradient Boosting emerged as the top-performing model, especially after hyperparameter optimization, achieving an AUC of 0.89 and F1-score of 0.62. Feature importance analysis further demonstrated the superior predictive value of engineered variables over traditional seismic metrics. The results validate the effectiveness of combining feature construction and ensemble learning for improved earthquake-induced GWL prediction and offer a scalable, interpretable approach for future seismic-hydrological modeling applications.