Using X-band Radar with a Neural Network to Forecast Combined Sewer Flow - A case study in Lund

Sammanfattning: This study aimed to forecast combined sewer flow into a wastewater treatment plant in Lund, Sweden by using uncalibrated X-band radar data with a neural network. Neural networks have proved themselves useful in the field of forecasting as they can solve multiple kinds of problems and recognise patterns in the data (Alemu et al. 2018) as well as model complex real-world problems (Zhang 2012). In 2018, an X-band radar unit was installed in the proximity of Lund which provides precipitation data with high spatial resolution, thus making it suitable for studying precipitation events on a smaller scale (Lengfeld et al. 2014). The study concluded that it is possible to accurately forecast combined sewer flow up to 60 minutes ahead of time by only using input variables connected to the catchment of the treatment plant. It was indicated that the prediction time could potentially be extended by adding forecasts of the precipitation as input to the network. The most important input variables were information about the sewage system, a nearby watercourse, the flow at the plant itself as well as information from a rain gauge. The radar is affected by attenuation, degrading the performance of the neural network during large flows.