Enhanced radial basis function neural networks for ozone level estimation

Ha, Q.P.; Wahid, Herman; H., Duc; Azzi, Merched

doi:10.1016/j.neucom.2014.12.048

Cited by 27 publications

(13 citation statements)

References 38 publications

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“…A Chemical Transport Model (CTM) based on Carbon Bond 5 photochemistry was incorporated into the TAPM-CTM model. This model has also been used for modeling air quality in the Greater Metropolitan Region in New South Wales, Australia [21][22][23]. The TAPM-CTM model was applied in HCMC to simulate the photochemical smog in HCMC in 2018, and in the Southern of Vietnam for simulating meteorology and air pollutants dispersion [24].…”

Section: Air Quality Modeling Using Tapm-ctm Systemmentioning

confidence: 99%

Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Hoang

et al. 2020

Atmosphere

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Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM2.5, NOx, and SO2. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO2 were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 µg/m3) and World Health Organization (WHO) (40 µg/m3). The annual average concentrations of PM2.5 were 23 µg/m3 and were also much higher than the WHO (10 µg/m3) standard by about 2.3 times. In terms of public health impacts, PM2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO2 and SO2 was 172 and 89 deaths, respectively. These figures demand some stringent measures from the authorities to potentially remedy the alarming situation of air pollution in HCM City.

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Section: Air Quality Modeling Using Tapm-ctm Systemmentioning

confidence: 99%

Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Hoang

et al. 2020

Atmosphere

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“…A majority of research work in indoor air quality is to obtain a mathematical model based on a given set of parameters and other information of geometry, shape, size, and contrast, see e.g., [36] to predict the pollutant distribution. On the other hand, inverse modelling generally focuses on the mathematical process of estimating the sources when determining the spatiotemporal distribution via a set of data or observations, see e.g., [37] for an outdoor emissions problem.…”

Section: Extended Fractional Kalman Filtermentioning

confidence: 99%

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Metia

Phung

2020

IEEE Sensors J.

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Multisensor fusion of air pollutant data in smart buildings remains an important input to address the well-being and comfort perceived by their inhabitants. An integrated sensing system is part of a smart building where real-time indoor air quality data are monitored round the clock using sensors and operating in the Internet-of-Things (IoT) environment. In this work, we propose an air quality management system merging indoor air quality index (IAQI) and humidex into an enhanced indoor air quality index (EIAQI) by using sensor data on a realtime basis. Here, indoor air pollutant levels are measured by a network of waspmote sensors while IAQI and humidex data are fused together using an extended fractional-order Kalman filter (EFKF). According to the obtained EIAQI, overall air quality alerts are provided in a timely fashion for accurate prediction with enhanced performance against measurement noise and nonlinearity. The estimation scheme is implemented by using the fractional-order modeling and control (FOMCON) toolbox. A case study is analysed to prove the effectiveness and validity of the proposed approach.

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“…However, most of these approaches use parameter values predefined without any justification, that is, in an empirical way. In [18], the spread parameter value of a neural network is selected by trial and error from a reasonably small interval previously determined.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm

Trejo-Caballero

Rostro-González

García-Capulín

et al. 2015

Mathematical Problems in Engineering

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Curve fitting is a very challenging problem that arises in a wide variety of scientific and engineering applications. Given a set of data points, possibly noisy, the goal is to build a compact representation of the curve that corresponds to the best estimate of the unknown underlying relationship between two variables. Despite the large number of methods available to tackle this problem, it remains challenging and elusive. In this paper, a new method to tackle such problem using strictly a linear combination of radial basis functions (RBFs) is proposed. To be more specific, we divide the parameter search space into linear and nonlinear parameter subspaces. We use a hierarchical genetic algorithm (HGA) to minimize a model selection criterion, which allows us to automatically and simultaneously determine the nonlinear parameters and then, by the least-squares method through Singular Value Decomposition method, to compute the linear parameters. The method is fully automatic and does not require subjective parameters, for example, smooth factor or centre locations, to perform the solution. In order to validate the efficacy of our approach, we perform an experimental study with several tests on benchmarks smooth functions. A comparative analysis with two successful methods based on RBF networks has been included.

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Enhanced radial basis function neural networks for ozone level estimation

Cited by 27 publications

References 38 publications

Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Sensing Data Fusion for Enhanced Indoor Air Quality Monitoring

Automatic Curve Fitting Based on Radial Basis Functions and a Hierarchical Genetic Algorithm

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