Abstract
Purpose This paper seeks to assess the performance levels of BA-GS-LSSVM compared to popular standalone algorithms used to build NO2 prediction models. The purpose of this paper is to pre-process a relatively large data of NO2 from Internet of Thing (IoT) sensors with time-corresponding weather and traffic data and to use the data to develop NO2 prediction models using BA-GS-LSSVM and popular standalone algorithms to allow for a fair comparison. Design/methodology/approach This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration. The authors used big data analytics infrastructure to retrieve the large volume of data collected in tens of seconds for over 5 months. Weather data from the UK meteorology department and traffic data from the department for transport were collected and merged for the corresponding time and location where the pollution sensors exist. Findings The results show that the hybrid BA-GS-LSSVM outperforms all other standalone machine learning predictive Model for NO2 pollution. Practical implications This paper's hybrid model provides a basis for giving an informed decision on the NO2 pollutant avoidance system. Originality/value This research installed and used data from 14 IoT emission sensors to develop machine learning predictive models for NO2 pollution concentration.
Highlights
Air pollution, a release of pollutants into the air, remains one of the significant challenges in the UK and globally, with over 25,000 associated deaths recorded yearly in the UK [1] and around 8.8 million deaths recorded globally [2]
The results show that the hybrid BA-GS-Least square support vector machine (LSSVM) outperforms all other standalone machine learning predictive Model for NO2 pollution
Given that feature selection may not be entirely favourable to some algorithms [11, 20], we developed the predictive models for each algorithm in two ways to allow fairer comparison
Summary
A release of pollutants into the air, remains one of the significant challenges in the UK and globally, with over 25,000 associated deaths recorded yearly in the UK [1] and around 8.8 million deaths recorded globally [2]. Apart from deaths, air pollution exposure can result in various short and long-term health challenges [3, 4]. Examples of short-term health challenges include eye pain, throat irritation, headaches, allergic reactions, and upper respiratory infections. Brain damage, liver damage, kidney damage, heart disease, respiratory disease, and suchlike are examples of long-term health challenges [5]. Aside from the severe impact of air pollutants on health, air pollution has significant consequences on the UK and the global economy. It costs the UK government approximately £40bn yearly [6] and around £3 trillion economic costs globally [7]. Recent studies by the centre for research on energy and clean air (CREA) links over 1.5 billion days of absence from
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