Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City

被引：1

作者：

Lang, Junwei ^{[1
]}

Zeng, Zhenjia ^{[1
,2
]}

Ma, Tengfei ^{[1
,3
]}

He, Sailing ^{[1
,2
,3
]}

机构：

[1] Zhejiang Univ, Coll Opt Sci & Engn, Ctr Opt & Electromagnet Res, Zhejiang Prov Key Lab Sensing Technol, Hangzhou 310058, Peoples R China

[2] Zhejiang Univ, Taizhou Res Inst, Taizhou 317700, Peoples R China

[3] Taizhou Agil Smart Technol Co Ltd, Taizhou 317700, Peoples R China

来源：

ALGORITHMS | 2023年 / 16卷 / 07期

基金：

中国国家自然科学基金;

关键词：

source tracking; multi-class classification; AERMOD; fully connected network; hybrid training strategy; DISPERSION PREDICTION; NEURAL-NETWORK; MODEL; EMISSION; AERMOD;

D O I：

10.3390/a16070342

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Sudden air pollution accidents (explosions, fires, leaks, etc.) in chemical industry parks may result in great harm to people's lives, property, and the ecological environment. A gas tracking network can monitor hazardous gas diffusion using traceability technology combined with sensors distributed within the scope of a chemical industry park. Such systems can automatically locate the source of pollutants in a timely manner and notify relevant departments to take major hazards into their control. However, tracing the source of the leak in a large area is still a tough problem, especially within an urban area. In this paper, the diffusion of 79 potential leaking sources with consideration of different weather conditions and complex urban terrain is simulated by AERMOD. Only 61 sensors are used to monitor the gas concentration within such a large scale. A fully connected network trained with a hybrid strategy is proposed to trace the leaking source effectively and robustly. Our proposed model reaches a final classification accuracy of 99.14%.

引用

页数：16