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<p>Flooding is a devastating natural disaster that often causes inestimable human and economic loss. With historical data, flood forecasting with machine learning methods is considered an effective way to evaluate the potential risks in the concerned region. In this work, the recursive feature elimination (RFE) using SHapley Additive exPlanations (SHAP) is employed to select informative features, which iteratively evaluates feature importance and eliminates the least important features. With the least significant feature discarded, a one-dimension-reduced feature vector is formed for the next iteration, the proposed machine learning model is trained with the input samples of updated features. After this recursive feature elimination procedure, the selected features are finalized by finding the iteration where the model’s trained performance is optimal or dramatically drops. With the proposed recursive feature elimination, the training time of the conventional machine learning models, such as XGB, RF, and CatBoost, can be reduced by 17.8%, 56.0%, 30.6% while keeping the loss of prediction accuracy below 1%, respectively. Furthermore, a hybrid machine learning model integrating the conventional Catboost model and Random Forest model is proposed to evaluate flood susceptibility in flood-prone areas of Malawi, where numerical experiments showed its superiority over the individual models.</p>

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A hybrid machine learning model for flood prediction with recursive feature elimination informed by training performance

  • 期刊论文

  • 发表日期：

    2026年2月18日

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  • Liying Gong,
  • Wai Lok Woo,
  • Yue Ivan Wu,
  • Xiujuan Zheng

摘要

Flooding is a devastating natural disaster that often causes inestimable human and economic loss. With historical data, flood forecasting with machine learning methods is considered an effective way to evaluate the potential risks in the concerned region. In this work, the recursive feature elimination (RFE) using SHapley Additive exPlanations (SHAP) is employed to select informative features, which iteratively evaluates feature importance and eliminates the least important features. With the least significant feature discarded, a one-dimension-reduced feature vector is formed for the next iteration, the proposed machine learning model is trained with the input samples of updated features. After this recursive feature elimination procedure, the selected features are finalized by finding the iteration where the model’s trained performance is optimal or dramatically drops. With the proposed recursive feature elimination, the training time of the conventional machine learning models, such as XGB, RF, and CatBoost, can be reduced by 17.8%, 56.0%, 30.6% while keeping the loss of prediction accuracy below 1%, respectively. Furthermore, a hybrid machine learning model integrating the conventional Catboost model and Random Forest model is proposed to evaluate flood susceptibility in flood-prone areas of Malawi, where numerical experiments showed its superiority over the individual models.