Time-series prediction and forecasting of ambient noise levels using deep learning and machine learning techniques

Ambient day and night noise levels prediction problems have traditionally been addressed using various statistical and machine learning methods. This paper presents the time-series predictions and forecasting of ambient noise levels using support vector machine (SVM) and deep learning method such as convolutional neural network (CNN) approach. This approach has been rarely reported for modeling ambient noise levels so far, although it has been widely used in air and water pollution predictions and forecasting. The study presents the applications of these techniques in time-series modeling of ambient day and night equivalent noise levels. A case study of ambient noise levels of one site each lying in commercial, residential, industrial and silence zone is presented. Ten-fold cross-validation is used in SVM model to train the model effectively and determine the optimized value of hyper-parameter (g, «, C). Also, CNN with a convolutional and pooling layer architecture framework is designed with optimum value of batch size, activation function, and filter size, among others. The validation and suitability of developed SVM and CNN models are ascertained by various statistical tests. Convolutional neural network approach is observed to outperform SVM model and thus can be a reliable approach for time-series modeling of ambient noise levels with a prediction error of 2.1 dB(A). The forecasting root mean squared error obtained for all the four zones using CNN model is observed to be less than 2.1 dB(A) for day equivalent noise levels and 1.9 dB(A) for night equivalent noise levels.