كليدواژه :
پيش‌بيني آلودگي هوا , ماشين بردار پشتيبان , جريان ترافيك و ذرات معلق , مديريت كيفيت هوا
چكيده فارسي :
يكي از مسائل حائز اهميت در شهرها و كلان شهرهايي كه با معضلات و آثار زيان بار آلودگي مواجه هستند، مسئله اطلاع رساني وضعيت آينده كيفيت هوا و ميزان آلودگي هواي شهري به مردم است. اين مهم مي تواند از طريق پيش بيني هاي روزانه يا حتي ساعتي وضعيت آلودگي هوا ميسر شود و از قرارگيري افراد جامعه در مكان هاي آلوده و تبعات جبران ناپذير آن جلوگيري كند. بنابراين نياز به پيش بيني وضعيت كيفي هوا و تخمين هاي كمي از غلظت آلاينده ها در پي آمدوشد وسايل نقليه احساس مي شود كه در اين پژوهش به مسئله پيش بيني ساعتي غلظت آلاينده ذرات معلق (pm2.5) در منطقه 11 شهرداري تهران پرداخته شده كه در حدود 80 درصد روزهاي آلوده سال تحت اثر اين آلاينده ار حد سالم تجاوز كرده است. روش مورد استفاده براي پيش بيني در اين پژوهش، يكي از روش هاي تحليل شبكه هاي عصبي با نام ماشين بردار پشتيبان (svm) است. نتايج حاصل از اين تحقيق نشان داد كه مدل هاي svm در پيش بيني سهم و مشاركت ترافيك ساعتي جاده اي در انتشار ذرات معلق به شدت خوب عمل مي كنند و پيش بيني ها به خوبي با مشاهدات هماهنگي دارند و اين فرصت را فراهم مي كند تا به عنوان ابزار مديريت كيفيت هوا به كار روند.
چكيده لاتين :
In recent decades, increasing population density and economic and industrial activities in metropolitan cities has increased traffic volumes and, consequently, increased levels of air pollution. The major source of air pollution in major developing cities is the massive transport of vehicles that use more than standard fuel and energy, and heavy traffic in the streets of these cities is often rooted in problems such as there is a lack of traffic management and traffic culture. One of the important issues in cities and metropolises that face pollution problems and harmful effects is the issue of informing about the future status of air quality and the amount of urban air pollution to the people. This can be achieved through daily or even hourly forecasts of air pollution and preventing people from being exposed to contaminated areas and their irreversible consequences. Therefore, the need to predict the quality of the air and the quantitative estimates of the concentration of pollutants in the aftermath of the equipment makes it felt that in this study, the problem of the predicted hourly concentration of particulate matter (PM2.5) in the district 11 municipalities of Tehran have exceeded 80% of the contaminated days under the influence of this pollutant. The difficulty and uncertainty associated with estimating and predicting the share of road traffic volume at the general level of air quality is the most important factor that can, if properly diagnosed, be very helpful. In order to take into account the effects of varying the volume of different traffic fleets in the process of changes in the concentration of pollutants and air pollution, it is necessary to pay attention to the effects of other influential variables including hydrological variables, geographical variables, etc. To achieve this, The methods of analytic analysis seem to be able to examine all of these effects together and in an omnipresent manner. The method used to predict this study is one of the methods for analyzing neural networks called Support Vector Machine (SVM). Artificial neural networks are important tools in the field of computational intelligence. Different types of artificial neural networks have been introduced, mainly in applications such as classification, clustering, pattern recognition, modeling and approximation of functions (or regression), control, estimation and optimization of the case Are used. Support Vector Machines (SVM) are a special type of neural network that, unlike other types of neural networks (such as multi-layer perceptron MLP and radial base functions of the RBF), instead of minimizing the error, minimize the operational risk of classification or modeling. Slowly This tool is very powerful and can be used in various fields such as classification, clustering and regression. The results of this study showed that SVM models work well in predicting the contribution and time share of road traffic in propagation of particulate matter, and predictions are well-coordinated with observations. It provides the opportunity to be used as an air quality management tool. Variable significance analysis results for SVM models provide this opportunity to be used as a tool for air quality management, in which the sensitivity of models to variations in emissions can be used to evaluate the effectiveness of a The air quality management scenario will test traffic fleet technology, combine the traffic fleet or its volume.
