Prediction of CSO Chamber Level Using Evolutionary Artificial Neural Networks

Abstract

Combined Sewer Overflows (CSOs) are a major source of pollution, spilling untreated wastewater directly into water bodies and/or the environment. If spills can be predicted in advance then interventions are available for mitigation. This paper presents Evolutionary Artificial Neural Network (EANN) models designed to predict water level in a CSO chamber up to 6 hours ahead using inputs of past CSO level, radar rainfall and rainfall forecast data. An evolutionary strategy algorithm is used to automatically select the optimal ANN input structure and parameters, allowing the ANN models to be constructed specifically for different CSO locations and forecast horizons. The methodology has been tested on a real world case study CSO and the EANN models were found to be superior to ANN models constructed using the trial and error method. This methodology can be easily applied to any CSO in a sewer network without substantial human input. It is envisioned that the EANN models could be beneficially used by water utilities for near real-time modelling of the water level in multiple CSOs and the generation of alerts for upcoming spills events.