Predicting inflow and infiltration to wastewater networks based on temperature measurements

Abstract: Sewer pipelines are deteriorating due to aging and sub optimal material selections, leading to the infiltration of clean ground and rainfall water into the pipes. It is estimated that a significant portion (up to 40-50%) of the water entering wastewater treatment plants is actually clean infiltrated water. This infiltration not only contributes to unnecessary energy consumption but also poses the risk of flooding the sewer network and treatment plants. Finding these broken pipes is utmost importance but is not straight forward due to the pipes being located a few meters below ground. There exist methods of pinpointing where these leaks occur, but they are often time consuming and expensive. This thesis seeks to address the following question; Can the estimation of infiltration be accomplished solely through the temperature data obtained from discrete pump stations, or is the inclusion of precipitation data essential for achieving accurate results? Two machine learning algorithms are investigated to solve the regression problem of estimating the amount of rainfall derived infiltration. The first model is a classical linear regression model. The second model is a Convolutional neural network (CNN). Both of these models are trained on the same data set. The temperatures recorded at the stations are reliable and can be trusted. However, the data labeling process involves utilizing calculated flows to the stations during both dry and wet weather periods. This means that the labels of the data cannot be trusted to be the actual ground truth, and there exists an uncertainty in the data set. Both models manage to capture large temperature drops which indicates infiltration has occurred. The linear regression model seems to be too sensitive towards small temperature drops and predicts infiltration when there is none. The CNN model on the other hand seems to be able to capture only large temperature drops when infiltration occurs. However, both models are trained with data from only one station, this means that the models are biased towards the average temperature of that particular station, other stations may have a higher or lower average temperature. When testing the models on a different station with lower average temperature the models predict infiltration when there is none.