Combined L-Band Polarimetric SAR and GPR Data to Develop Models for Leak Detection in the Water Pipeline Networks

Water pipeline leak detection in a fast and accurate way is of much importance for water utility companies and the general public. At present, the rapid development of remote sensing and computer technologies makes it possible to detect water pipeline leaks on a large scale efficiently and timely. The leakage will cause an increase in the water content and dielectric constant of the soil around the pipeline, so it is feasible to determine the leakage site by measuring the subsurface soil relative dielectric constant (SSRDC). In this paper, we combine the SAOCOM-1A L-band synthetic-aperture radar (SAR) and the ground-penetrating radar (GPR) data to develop regression models that predict the SSRDC values. The model features are selected with the Boruta wrapper algorithm based on the SAOCOM-1A images after pre-processing, and the SSRDC values at sampling locations within the research area are calculated with the reflected wave method based on the GPR data. We evaluate multiple linear regression (MLR), random forest (RF), and multi-layer perceptron neural network (MLPNN) models for their ability to predict the SSRDC values using the selected features. The experimental results show that the MLPNN model (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.705, RMSE = 1.936, MAE = 1.664) can better estimate the SSRDC values. Further, in the main urban area of Tianjin, China, which has a large water pipeline system, the SSDRC values of the area are obtained with the best model, and the locations where the predicted SSDRC values exceeded a certain threshold were considered potential leak locations. The empirical results indicate an encouraging potential of the proposed method to locate the pipeline leaks. This will provide a new avenue for the monitoring and treatment of water pipeline leaks.