Real-Time Detection of Sanitary Sewer Overflows Using Neural Networks and Time Series Analysis

Abstract

Sanitary sewer overflows (SSOs) are becoming of increasing concern as a health risk. Utilities and regulators have taken preventive measures but many overflows still occur and are not identifiable, especially in access-challenged locations. Several mathematical approaches are presented for detecting if a disruption in the system is impending or occurring based on measurements at one or more locations in the system. Time series analysis and neural networks are used as prediction tools for expected depths and flows for single measurement locations and a neural network is developed for a multiple monitor system. Control limit theory is applied in all cases for identifying significant deviations of measured values from the expected values that suggest a SSO is occurring. Data from Pima County Wastewater Management’s monitoring system are used in two case studies.