Preventing overfitting in deep learning model applied to road cave-in risk assessment using sewer facility data

Abstract

To prevent road cave-ins in advance, we constructed a supervised deep learning model (Multi-Layer Perceptron: MLP) using 9 variables and applied it to the real field to assess the risk of road cave-ins. This deep learning model had issues with overfitting, so we tried to resolve the problem by properly controlling the number of iterations after determining the optimal hyperparameters. Consequently, we were able to mitigate overfitting with a slight decrease in accuracy of less than 3% compared to the evaluation metrics during training and reduce the false-negative error of road cave-ins by up to 17%. Moreover, excluding variables of categorical and different attribute information did not significantly affect the accuracy, sug- gesting the potential for efficiency in collecting input information. In the future, it is desirable to update and accumulate supervised data to construct a more accurate prediction model.