Assessment of rainfall-derived inflow and infiltration in sewer systems with machine learning approaches

Abstract

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events. HIGHLIGHTS Machine learning models, particularly the LSTM model, outperformed traditional methods in estimating RDII in sewer systems.; Feature engineering techniques allowed for the extraction of physically significant features in sewer flow modelling.; Machine learning models successfully simulated the temporal variation in RDII processes, resulting in improved accuracy for predicting peak flows and RDII volumes during storm events.;

Keywords

• long short-term memory
• machine learning
• rainfall-derived inflow and infiltration
• random forest
• sewer flow prediction