سیستم هشدار زودهنگام آلودگی هوا
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سیستم هشدار زودهنگام آلودگی هوا

عنوان فارسی مقاله: تحقیق و کاربرد مدل پیش بینی ترکیبی مبتنی بر حذف نویز ثانویه و بهینه سازی چند هدفه برای سیستم هشدار زودهنگام آلودگی هوا
عنوان انگلیسی مقاله: Research and application of the hybrid forecasting model based on secondary denoising and multi-objective optimization for air pollution early warning system
مجله/کنفرانس: مجله تولید پاک – Journal of Cleaner Production
رشته های تحصیلی مرتبط: مهندسی محیط زیست، مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: آلودگی هوا، معماری سیستم های کامپیوتری
کلمات کلیدی فارسی: سیستم هشدار زودهنگام، پیش بینی آلودگی هوا، حذف نویز ثانویه، الگوریتم بهینه سازی چند هدفه، ارزیابی مصنوعی فازی
کلمات کلیدی انگلیسی: Early warning system، Air pollution forecasting، Secondary denoising، Multi-objective optimization algorithm، Fuzzy synthetic evaluation
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.jclepro.2019.06.201
دانشگاه: School of Statistics, Dongbei University of Finance and Economics, Dalian, 116023, PR China
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 7.096 در سال 2018
شاخص H_index: 150 در سال 2019
شاخص SJR: 1.620 در سال 2018
شناسه ISSN: 0959-6526
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 17
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
آیا این مقاله مدل مفهومی دارد: دارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E12763
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


Nomenclature


1. Introduction


2. Preprocessing of air pollution concentration data


3. Introduction of multi-objective optimization algorithm and forecasting algorithm


4. Air quality assessment and application of air pollution early warning system


5. Discussion


6. Conclusion


Acknowledgement


References

نمونه متن انگلیسی مقاله

Abstract


With the increasing irreversible damage caused by air pollution, an early warning system to send warning information to human beings so that they can avoid more harm caused by air pollution is required. A reliable warning system can provide valuable information to protect mankind from the effects of pollution and can act as a tool that allows regulators to implement corresponding measures to reduce air pollution. However, the previous most valuable research studies were focused on pollution forecasting and the extent to which pollution affects health, and the aim of only a few studies was to analyze pollution from an application perspective and to construct a reasonable early warning system. In this study, an air pollution early warning system was constructed, which comprises two modules: an air pollution forecasting module and an air quality evaluation module. In the forecasting module, two denoising methods and a multi-objective optimization algorithm are integrated into a novel hybrid forecasting model. In the evaluation module, fuzzy synthetic evaluation is used to evaluate air quality objectively. To verify the performance of the proposed early warning system, hourly pollutants concentration data were used in a case study of three metropolises in China and three numeric simulation experiments were conducted. The simulation results show that the forecasting performance of the L2,1RF-ELM model used in this study is better than the traditional neural network, and the forecasting model proposed in this paper is better than the traditional statistical model ARIMA. Moreover,the early warning system performed well in terms of highly accurate forecasting and accurate evaluation in the three research areas.


Introduction


For nearly a century, the rapid development of industrialization and urbanization has increased the amount of energy consumed by human activities and caused serious air pollution in the world. Scholars have conducted a significant number of air pollution research studies. The results of extensive studies indicate that exposure to air pollution can cause a variety of diseases (Cohen et al., 2017; Guo et al., 2016). Moreover, air pollution can also be detrimental to the ecosystem, leading to the greenhouse effect, ozone layer destruction, acid rain, reduced solar radiation, etc. (Anwar et al., 2016; Desonie, 2007; Ramanathan and Feng, 2009). Therefore, accurate and authentic air quality information is increasingly needed to enable industries to minimize their production of pollutants and residents to adjust their activities promptly to mitigate the damage caused by major pollution. To diminish the effects of air pollution, scholars have focused on analyzing and forecasting the concentrations of pollutants, devoting their efforts to providing highly accurate forecasting. During the past one hundred years, many forecasting methods were proposed, the most popular of which can be classified into three categories: physical, statistical, and artificial intelligence models (Bai et al., 2018). Physical models use the physicochemical process of pollutants in the atmosphere as the entry point for forecasting pollutant concentrations. Statistical methods can be divided into causal models and time series models according to their fundamental characteristics. The assumption of causal models is that the historical relationship between dependent and independent variables will remain valid in the future.

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