Data-Driven BOD5 Prediction in Urban Drainage Networks Using a BP Neural Network Under Limited Monitoring Conditions

Abstract

To address the engineering challenges of long detection cycles and the inability to provide real-time early warning for the traditional measurement of 5-day biochemical oxygen demand (BOD5) in drainage networks, coupled with the limited availability of monitored indicators in practice, this study proposes a BOD5 concentration prediction method based on a BP neural network. Based on 10,511 sets of field measurements from a city’s drainage network, only three routinely measurable indicators, chemical oxygen demand (COD), ammonia nitrogen (NH4+-N), and total phosphorus (TP), were selected as input features. A COD-only baseline model was first constructed to validate the reliability of the core predictive factor (R2 = 0.9401). NH4+-N and TP were then incorporated to build a three-feature synergistic prediction model, which improved the R2 to 0.9541. By comparing three optimization algorithms (L-BFGS, Adam, and SGD) and performing hyperparameter optimization, the final test-set R2 reached 0.957 with an MAE of 8.834 mg/L (MAPE = 12.89%), representing a practical error of approximately 10.3% of the mean BOD5 concentration. The prediction accuracy was significantly superior to support vector regression (SVR, R2 = 0.930) and multiple linear regression (MLR, R2 = 0.955). Generalization ability was validated using three external independent datasets from Jinan (China), Hohhot (China), and Melbourne (Australia). The results show that the model maintains reasonable predictive capability within the same region (R2= 0.844), but accuracy declines substantially when transferring across regions (MAPE increasing from 12.89% to 35.07–58.63%) and countries. This method provides a high-accuracy, low-cost, real-time estimation scheme for early warning of water quality in drainage networks under limited monitoring conditions.