Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection

Zhu, Zhengqiu; Qiu, Sihang; Chen, Bin; Wang, Rongxiao; Qiu, Xiaogang

doi:10.3390/ijerph15081640

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The results from both methods were evaluated using analysis of variance (ANOVA) to obtain the gas concentration at the source, and using a Gaussian dispersion model to analyze the gas dispersion. In the Gaussian dispersion model, parameters such as wind speed and direction, source term, etc., were obtained by monitoring data to acquire positioned trajectories with bivariate input environmental data [23,24]. In a small scale of gas measurement (<100 m), a sparse Gaussian kernel method was used as a statistical evaluation on a two-dimensional spatial model of a grid pattern to deal with the specific properties of gas dispersion, including the turbulent features of the wind [25,26].…”

Section: Introductionmentioning

confidence: 99%

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

Kuantama

Ţarcă

Dziţac

et al. 2019

Sensors

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This study presents a detailed analysis of an air monitoring development system using quadcopters. The data collecting method is based on gas dispersion investigation to pinpoint the gas source location and determine the gas concentration level. Due to its flexibility and low cost, a quadcopter was integrated with air monitoring sensors to collect the required data. The analysis started with the sensor placement on the quadcopter and their correlation with the generated vortex. The reliability and response time of the sensor used determine the duration of the data collection process. The dynamic nature of the environment makes the technique of air monitoring of topmost concern. The pattern method has been adapted to the data collection process in which area scanning was marked using a point of interest or grid point. The experiments were done by manipulating a carbon monoxide (CO) source, with data readings being made in two ways: point source with eight sampling points arranged in a square pattern, and non-point source with 24 sampling points in a grid pattern. The quadcopter collected data while in a hover state with 10 s sampling times at each point. The analysis of variance method (ANOVA) was also used as the statistical algorithm to analyze the vector of gas dispersion. In order to tackle the uncertainty of wind, a bivariate Gaussian kernel analysis was used to get an estimation of the gas source area. The result showed that the grid pattern measurement was useful in obtaining more accurate data of the gas source location and the gas concentration. The vortex field generated by the propeller was used to speed up the accumulation of the gas particles to the sensor. The dynamic nature of the wind caused the gas flow vector to change constantly. Thus, more sampling points were preferred, to improve the accuracy of the gas source location prediction.

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Section: Introductionmentioning

confidence: 99%

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

Kuantama

Ţarcă

Dziţac

et al. 2019

Sensors

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“…The mechanism-based model cannot adapt to the dynamic meteorological environment. In order to solve this problem, the particle filter is used as a data assimilation method to construct a dynamic data-driven Gaussian dispersion model [31]. In this way, the mechanism model is corrected by the dynamic monitoring data.…”

Section: Data Assimilation Framework Based On a Particle Filtermentioning

confidence: 99%

The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion

Chen

Wang

et al. 2020

Symmetry

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With the profound understanding of the world, modeling and simulation has been used to solve the problems of complex systems. Generally, mechanism-models are often used to model the engineering systems following the Newton laws, and this kind of modeling approach is called white-box modeling; however, when the internal structure and characteristics of some systems are hard to understand, the black-box modeling based on statistic and data-modeling is often used. For most complex real systems, a single modeling approach can hardly describe the target system accurately. In this paper, we firstly discuss and compare the white-box and black-box modeling approaches. Then, to mitigate the limitations of these two modeling methods in mechanism-partially-observed systems, the gray-box based modeling approach integrating both a mechanism model and data model is proposed. In order to explain the idea of gray-box based modeling, the atmosphere dispersion modeling is studied in practical cases from two symmetric aspects. Specifically, the framework of data assimilation is used to illustrate the modeling from white-box to gray-box, while the Gauss features based Support Vector Regression (SVR) models are used to illustrate the modeling from black-box to gray-box. To verify the feasibility of the gray-box modeling method, we conducted both simulation experiments and real dataset symmetry experiments. The experiment results show the enhanced performance of the gray-box based modeling approach. In the end, we expect that this gray-box based modeling approach will be an alternative modeling approach for different existing systems.

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Data assimilation method for quantifying controlled methane releases using a drone and ground-sensors

Darynova

Blanco

Juery

et al. 2023

Atmospheric Environment: X

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Data-Driven Hazardous Gas Dispersion Modeling Using the Integration of Particle Filtering and Error Propagation Detection

Cited by 3 publications

References 38 publications

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

The Design and Experimental Development of Air Scanning Using a Sniffer Quadcopter

The Gray-Box Based Modeling Approach Integrating Both Mechanism-Model and Data-Model: The Case of Atmospheric Contaminant Dispersion

Data assimilation method for quantifying controlled methane releases using a drone and ground-sensors

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