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Hybrid SARIMAX–LSTM Forecasting of Hourly Water Reduce Ratio with ROC-Based Risk Threshold Optimization

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(8), pp.147~157
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : May 21, 2026
  • Accepted : July 16, 2026
  • Published : August 31, 2026

Joon-Yong Kim 1

1서울신학대학교

Accredited

ABSTRACT

Hourly water reduce ratio (WRR), defined here as the hourly rate of change in relative humidity, is used as an environmental proxy for rapid moisture-condition change relevant to fresh-concrete quality management. This study proposes a one-hour-ahead SARIMAX-LSTM hybrid forecasting framework in which SARIMAX models seasonal-linear dynamics and lagged weather effects, and LSTM corrects nonlinear residual components. The analysis uses 1,488 hourly observations from station ID 1189 between July 1 and September 1, 2021. Timestamp alignment, duplicate handling, missing-data treatment, and outlier auditing were explicitly specified. Wind speed was evaluated as a candidate exogenous variable, but it was not retained because it did not improve validation RMSE and MAE consistently. The final hybrid model achieved RMSE=4.413 and MAE=3.117 on the test set, representing numerical reductions of 1.66-5.99% in RMSE and 2.42-2.59% in MAE relative to the baselines. However, Diebold-Mariano tests with Holm correction and moving-block bootstrap confidence intervals did not establish statistical significance at the 0.05 level. ROC-based threshold optimization was conducted on the validation set using Youden's J and F1-score. Under WRR <= -5%, the selected alarm threshold was -0.479%/h and yielded Recall=0.926, Precision=0.210, F1=0.342, and ROC-AUC=0.788 on the test set. The framework is therefore suitable for early warning and secondary inspection support, not for direct diagnosis of concrete strength loss.

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