Published Jul 07, 2023
Pollution caused by noise has been proven to cause some of the most harmful health hazards in the present world. Source of such noise could be varied: human malpractices, loudspeakers during festivals, operation of industrial machinery, traffic honking, and so on. Highway noise alone affects more than 100 million people worldwide. Typically, we categorize unwanted sounds, i.e noise, by their intensity, frequency, periodicity, and duration. People living in high noise environments have adapted with their surroundings for years without realizing the impact of noise and making any conscious step towards mitigating their health and lifestyle degradation. The physical, mental, and psychological effects of improper sleep are fatal.
Noise pollution impacts people in some areas more than others. People facing socio economic hardships are more prone to such hazards than society’s elites living in better served and maintained localities. Such disparities have led to insufficient and unfair policies that benefit the elite more than the suffering ‘others.’ Thus, we need to build risk analysis instruments that can inform policymakers on the national and global platform in regulating health risks caused due to avoidable noise and better serve our communities at large, especially the marginalized age and ethnic groups. The World Health Organisation (WHO) has also expressed concerns regarding the same from time to time.
There have been many prominent techniques and findings published in this domain in the last two decades. In the experiments of S. Kuwano and T. Mizunami (2002), it is shown that the type of noise is an important factor in deciding the sleep disturbance. Meaningful noises disturbs people more whereas if the noise is meaningless then people get disturbed only when the noise level is high. In a study by Tayal, Devendra K., Amita Jain, and Vinita Gupta “Fuzzy Expert System for Noise Induced Sleep Disturbance and Health Effects” (2010), various noise factors which can have a significant impact on Sleep and health have been identified. They developed and implemented the MIMO Expert system using fuzzification and defuzzification strategies that predicts the health effect, health condition in the morning and sleep disturbance, taking five different types of input variables viz. type of noise, age, short noise duration, long noise duration, and noise level. A study by Sujit Kumar De, Bijay Kumar Swain, et al (2017) developed a fuzzy logic to analyze the noise risk, and then it has been compared by the odds ratio of the experimental data. Gulliver et al. (2015) developed a noise exposure model to identify the best possible level of human health. Prakash and Veerappa (2015) were able to interpret the effects of noise pollution on human beings, using fuzzy logic techniques. On the other hand, with the help of precautionary principle, the problem of risk management was studied by Cameron and Peloso (2005). The authors such as Haimes (2009) and Takas (2010) have developed several models over multilevel fuzzy approach to risk and disaster management for studying the noise annoyance. Shivdev, Nagarajappa, Lokeshappa, and Kusagur (2015) discussed empirically a noise pollution model studying health risks with affected people of different ages. Zaheeruddin et al. (2003) studied a fuzzy modeling of human work efficiency in a noisy environment. . Eberhardt (1987, 1990) studied the effects of road traffic noise on sleep in the home for school children who lived along streets with night traffic. Shivdev, P. P., et al. “Fuzzy logic technique for noise induced health effects in mine site” (2015) designed a fuzzy noise prediction model to study health impacts of prolonged exposure of miners to the high levels of noise. In the recent past, several computing efforts have also been made in the same area using machine learning techniques such as Wen, Po-Jiun, and Chihpin Huang. “Noise prediction using machine learning with measurements analysis” Applied Sciences (2020).
Developing techniques to study health issues using fuzzy logic is of greater interest to me due to its flexible nature and proximity to real world applications. Notion of fuzzy sets, as opposed to crisp binary sets, is able to closely model the human cognition and thinking process. Thus, studying the dynamic nature of impact of environmental and man-made factors on one’s physical, mental, and psychological health using fuzzy logic is relevant. It can give answers to problem statements that are ambiguous, uncertain, and not crisply defined: like real world problems.