Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks

Abstract

In a deep learning model, the effect of the model may vary depending on the setting of the hyperparameters. Despite the importance of such hyperparameter determination, most previous studies related to burst detection models of the water supply pipe network used hyperparameters applied in other fields as-is or made a trial-and-error setting based on experience, which is a limitation. In this paper, a study was conducted on the deep learning hyperparameter determination of a deep neural network (DNN)-based real-time detection model of pipe burst accidents. The pipe burst model predicted water pressure by using operation data in units of 1 min, and the data period applied for the model training was less than 1 month (1, 2, and 3 weeks) in order to consider frequent changes in the system. A sensitivity analysis was first performed on the type of activation function and the period of the learning data, which may have different effects depending on the characteristics of the target problem. The number of hidden layers related to the network structure and the number of neurons in each hidden layer were set as hyperparameters for additional sensitivity analysis. The sensitivity analysis results were derived and compared using four quantified prediction error indicators. In addition, the model running time was analyzed to evaluate the practical applicability of the development model. From the results, it was confirmed that excellent effects could be expected if using a rectifier function as the activation function, 144 nodes in the hidden layer, which is eight times the number of nodes in the input layer, and four hidden layers. Additionally, by analyzing the appropriate period of training data required for model pressure prediction through prediction error and driving time, it was confirmed that it was most appropriate to use the data of two weeks. By applying the hyperparameter values determined through detailed sensitivity analysis and by applying the data of one week including actual burst accidents to the built-up pressure prediction model, the accident detection and predictive performance of the model were verified. The rational determination of the period of input factors for the optimal hyperparameter setting and model building, as in this study, is very necessary and very important as it can serve to ensure the continuity of the operation effects of the deep learning model.