Intelligent prediction and early warning model for substation groundwater depth based on machine learning

Abstract

[Objective] Designing an accurate and efficient groundwater depth dynamics prediction model is crucial for the effective application of intelligent monitoring and early warning systems for substation drainage systems and for ensuring the safe and stable operation of substations. [Methods] Focusing on the pilot study project of the 220kV substation in the industrial park, a comprehensive evaluation was conducted on three machine learning models: Extreme Gradient Boosting(XGBoost), Random Forest(RF), and Long Short-Term Memory(LSTM). The performance of these models in predicting groundwater depth dynamics under heavy rainfall scenarios was analyzed in detail. The training data for the models were derived from a calibrated and validated groundwater flow numerical model, using prediction result of groundwater depth dynamics under various rainfall scenarios as benchmark reference values. To thoroughly assess the prediction accuracy and reliability of these models, the Nash-Sutcliffe Efficiency Coefficient(NSE), Root Mean Square Error(RMSE), Pearson Correlation Coefficient, and Mean Absolute Error(AE) were used as evaluation indicators. [Results] The research result showed that XGBoost, RF, and LSTM models could simulate groundwater depth dynamics consistent with the benchmark result over the time scale, with NSE, RMSE, and Pearson correlation coefficients reaching 0.999 8, 0.003 1 m, and 0.999 9, respectively. However, the spatial performance varied significantly. The AE simulated by the RF model was less than 0.01 m, the AE simulated by the XGBoost model was less than 0.26 m, and the AE given by the LSTM model was less than 0.12 m. When using model data from 20% of the grid points for machine learning training, the RF model still showed the best performance, and the time efficiency of model training and prediction improved by 5 times. [Conclusion] The groundwater depth dynamics prediction model based on machine learning models demonstrates excellent performance and shows promising application prospects in the intelligent monitoring and early warning systems for drainage systems.

Keywords

substation / groundwater depth early warning system / machine learning / intelligent monitoring / numerical simulation