کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8864054 1620296 2018 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Atmospheric dispersion prediction and source estimation of hazardous gas using artificial neural network, particle swarm optimization and expectation maximization
ترجمه فارسی عنوان
برآورد پراکندگی اتمسفری و برآورد منبع گاز خطرناک با استفاده از شبکه عصبی مصنوعی، بهینه سازی ذرات و به حداکثر رساندن انتظارات
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات علم هواشناسی
چکیده انگلیسی
Hazardous gas leak accident has posed a potential threat to human beings. Predicting atmospheric dispersion and estimating its source become increasingly important in emergency management. Current dispersion prediction and source estimation models cannot satisfy the requirement of emergency management because they are not equipped with high efficiency and accuracy at the same time. In this paper, we develop a fast and accurate dispersion prediction and source estimation method based on artificial neural network (ANN), particle swarm optimization (PSO) and expectation maximization (EM). The novel method uses a large amount of pre-determined scenarios to train the ANN for dispersion prediction, so that the ANN can predict concentration distribution accurately and efficiently. PSO and EM are applied for estimating the source parameters, which can effectively accelerate the process of convergence. The method is verified by the Indianapolis field study with a SF6 release source. The results demonstrate the effectiveness of the method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Atmospheric Environment - Volume 178, April 2018, Pages 158-163
نویسندگان
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