A Committee Evolutionary Neural Network for the Prediction of Combined Sewer Overflows

Combined Sewer Overflows (CSOs) are a major source of pollution and urban flooding, spilling untreated wastewater directly into water bodies and the surrounding environment. If overflows can be predicted sufficiently in advance, then techniques are available for mitigation. This paper presents a novel bi-model committee evolutionary artificial neural network (CEANN) designed to forecast water level in a CSO chamber from 15 min to 6 h ahead using inputs of past/current CSO level data, radar rainfall data and forecast forecasted rainfall data. The model is composed of two evolutionary artificial neural network (EANN) models. The two models are trained and optimised for wet and dry weather conditions respectively and their results combined into a single response using a non-linear weighted averaging approach. An evolutionary strategy algorithm is employed to automatically select the optimal artificial neural network (ANN) structure and parameter set, allowing the network to be tailored specifically for different CSO locations and forecast horizons without significant human input. The CEANN model was tested and evaluated on real level data from 4 CSOs located in Northern England and the results compared to three other ANN models. The results demonstrate that the CEANN model is superior in terms of accuracy for almost all forecast horizons considered. It is able to accurately forecast the dry weather and wet weather level, predicting the timing and magnitude of upcoming spill events, thus providing information that is of clear use to a wastewater utility.