مقاله انگلیسی رایگان در مورد سیستم هشدار زودهنگام آلودگی هوا – الزویر ۲۰۱۹

elsevier

 

مشخصات مقاله
ترجمه عنوان 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
انتشار مقاله سال ۲۰۱۹
تعداد صفحات مقاله انگلیسی ۱۷ صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research Article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) Scopus – Master Journals List – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۷٫۰۹۶ در سال ۲۰۱۸
شاخص H_index ۱۵۰ در سال ۲۰۱۹
شاخص SJR ۱٫۶۲۰ در سال ۲۰۱۸
شناسه ISSN ۰۹۵۹-۶۵۲۶
شاخص Quartile (چارک) Q1 در سال ۲۰۱۸
مدل مفهومی دارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط مهندسی محیط زیست، مهندسی کامپیوتر
گرایش های مرتبط آلودگی هوا، معماری سیستم های کامپیوتری
نوع ارائه مقاله
ژورنال
مجله / کنفرانس مجله تولید پاک – Journal of Cleaner Production
دانشگاه  School of Statistics, Dongbei University of Finance and Economics, Dalian, 116023, PR China
کلمات کلیدی 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
شناسه دیجیتال – doi
https://doi.org/10.1016/j.jclepro.2019.06.201
کد محصول  E12763
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:
Abstract
Nomenclature
۱٫ Introduction
۲٫ Preprocessing of air pollution concentration data
۳٫ Introduction of multi-objective optimization algorithm and forecasting algorithm
۴٫ Air quality assessment and application of air pollution early warning system
۵٫ Discussion
۶٫ Conclusion
Acknowledgement
References

 

بخشی از متن 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.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد.