Journal Article

A hybrid deep leaning model for prediction and parametric sensitivity analysis of noise annoyance

Kumaraswamidhas, Lakshmi Annamalai, Rehman, Masood ur, Prince, Kamal, Mustafa, Tiwari, Shashi Kant

2023

Overview

Noise annoyance is recognized as an expression of physiological and psychological strain in acoustical environment. The studies on prediction of noise annoyance and parametric sensitivity analysis of factors affecting it have been rarely reported in India. A hybrid ConvLSTM technique was developed in the study to predict traffic-induced noise annoyance in 484 people based on ambient noise levels, as well as survey information. Ambient noise levels were obtained at different locations of Dhanbad city using sound level meter at varying intervals, viz. 09AM–12PM, 03PM–06PM, and 08PM–11PM. The proposed method was compared with some well-known neural network techniques such as K-nearest neighbors (KNN), artificial neural network (ANN), recurrent neural network (RNN), and long-short-term memory (LSTM). The experimental results indicate that the proposed method outperforms other techniques and can be a reliable approach for prediction of noise annoyance with an accuracy of 93.8%. It can be concluded from noise maps that the noise levels in all locations of the Dhanbad city were higher than 70 dB(A) and noise sensitivity is the most important input variable of traffic-induced noise annoyance, followed by honking noise, education, exposure hours, L Aeq , sleeping disorder, and chronic disease. The study shall facilitate in developing a decision support tool for prediction of noise annoyance and promoting implementation of suitable public policy in urban cities.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Acoustics / Aquatic Pollution / Artificial neural networks / Atmospheric Protection/Air Quality Control/Air Pollution / chronic diseases / Chronic illnesses / Cities / Decision support systems / Earth and Environmental Science / Ecotoxicology / education / Environment / Environmental Chemistry / Environmental Exposure / Environmental Health / Environmental science / Humans / hybrids / India / Long short-term memory / Neural networks / Noise / Noise levels / Noise prediction / Noise sensitivity / Noise, Transportation / Parameter sensitivity / people / prediction / Predictions / Public policy / Recurrent neural networks / Research Article / Sensitivity analysis / sound level meters / surveys / Surveys and Questionnaires / Waste Water Technology / Water Management / Water Pollution Control