Environmental Noise Pollution Forecasting using Fuzzy-autoregressive Integrated Moving Average Modelling

Abstract

Predicting noise pollution from building sites is important to take precautions to avoid pollution that harms the public. A high accuracy of the prediction model is required so that the predicted model can reach the true value. Forecasting models must be built on solid historical data to achieve high forecasting accuracy. However, data collected through various approaches are subject to ambiguity and uncertainty, resulting in less reliable predictive models. Therefore, the data must be handled accurately, to eliminate data uncertainty. Standard data processing processes are easy to use but do not provide a consistent method for dealing with this ambiguous data. Therefore, a method to deal with data containing uncertainty for forecasting purposes is presented in this paper. A new technique for providing uncertainty-based data preparation has been employed to develop an ARIMA-based model of environmental noise pollution. During the data preparation stage, the standard deviation approach was used. Prior to the development of the prediction model, it is crucial to manage the fuzzy data to minimize errors. The experimental findings show that the suggested data preparation strategy can increase the model's accuracy