Improving the control of water treatment plant with remote sensing-based water quality forecasting model

When Total Organic Carbon (TOC) in the source water is in contact with disinfectants in a drinking water treatment process, it oftentimes causes the formation of disinfection byproducts such as Trihalomethanes which have harmful effects on human health. As a result of the potential health risk of Trihalomethanes for drinking water, proper monitoring and forecasting of high TOC episodes in the source water body can be helpful for the operators who are in charge of the decisions when they have to start the removal procedures for TOC in surface water treatment plants. This issue is of great importance in Lake Mead in the United States which provides drinking water for 25 million people, while it is considered as an important recreational area and wildlife habitat as well. In this study, artificial neural network, extreme learning machine, and genetic programming are examined using the long-term observations of TOC concentration throughout the lake. Among these models, the model with the best performance was applied in the development of a forecasting model to predict TOC values on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory of the past states with nonlinear autoregressive neural network with external input (NARXNET) on a rolling basis onwards. The best input scenario of NARXNET was selected with respect to several statistical indices. Numerical outputs of the forecasting process confirm the fidelity of the iterative scheme in predicting water quality status one day ahead of the time.