| مشخصات مقاله | |
| ترجمه عنوان مقاله | تحلیل کلان داده ها در پیش بینی وضعیت آب و هوا: یک بررسی اصولی |
| عنوان انگلیسی مقاله | Big Data Analytics in Weather Forecasting: A Systematic Review |
| انتشار | مقاله سال ۲۰۲۱ |
| تعداد صفحات مقاله انگلیسی | ۲۹ صفحه |
| هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
| پایگاه داده | نشریه الزویر |
| نوع نگارش مقاله |
مقاله مروری (Review Article) |
| مقاله بیس | این مقاله بیس نمیباشد |
| نمایه (index) | ISC MasterList – Master Journals List – JCR |
| نوع مقاله | ISI |
| فرمت مقاله انگلیسی | |
| ایمپکت فاکتور(IF) |
۷٫۳۰۲ در سال ۲۰۲۰ |
| شناسه ISSN | ۱۸۸۶-۱۷۸۴ |
| شاخص Quartile (چارک) | Q1 در سال ۲۰۲۰ |
| فرضیه | ندارد |
| مدل مفهومی | ندارد |
| پرسشنامه | ندارد |
| متغیر | ندارد |
| رفرنس | دارد |
| رشته های مرتبط | مهندسی کامپیوتر، جغرافیا |
| گرایش های مرتبط | هوش مصنوعی، آب و هواشناسی |
| نوع ارائه مقاله |
ژورنال |
| مجله | Archives of Computational Methods in Engineering – آرشیو روش های محاسباتی در مهندسی |
| دانشگاه | Islamic Azad University, Tehran, Iran |
| شناسه دیجیتال – doi |
https://doi.org/10.1007/s11831-021-09616-4 |
| کد محصول | E15948 |
| 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 Introduction Background Related Works and Motivation Research Methodology Classifcation of the Selected Approaches Discussion Open Issues and Future Trends Conclusion and Limitation References |
| بخشی از متن مقاله: |
| Abstract Weather forecasting, as an important and indispensable procedure in people’s daily lives, evaluates the alteration happening in the current condition of the atmosphere. Big data analytics is the process of analyzing big data to extract the concealed patterns and applicable information that can yield better results. Nowadays, several parts of society are interested in big data, and the meteorological institute is not excluded. Therefore, big data analytics will give better results in weather forecasting and will help forecasters to forecast weather more accurately. In order to achieve this goal and to recommend favorable solutions, several big data techniques and technologies have been suggested to manage and analyze the huge volume of weather data from diferent resources. By employing big data analytics in weather forecasting, the challenges related to traditional data management techniques and technology can be solved. This paper tenders a systematic literature review method for big data analytic approaches in weather forecasting (published between 2014 and August 2020). A feasible taxonomy of the current reviewed papers is proposed as technique-based, technology-based, and hybrid approaches. Moreover, this paper presents a comparison of the aforementioned categories regarding accuracy, scalability, execution time, and other Quality of Service factors. The types of algorithms, measurement environments, modeling tools, and the advantages and disadvantages per paper are extracted. In addition, open issues and future trends are debated. introduction Originally weather forecasting started in the nineteenth century [1, 2]. The analysis of atmospheric data, including temperature, radiation, air pressure, wind speed, wind direction, humidity, and rainfall, is defned as weather forecasting. In order to predict the weather, a high volume of data must be collected or generated. Furthermore, these data are disorganized. Thus, utilizing the weather data for predicting the weather is a complex task, and it contains too many changeable parameters. These parameters vary according to the weather conditions that change very fast. To propose an algorithm for weather forecasting, we should consider its particular characteristics, such as continuity, data intensity, and multidimensional and chaotic behaviors [3, 4]. Originally weather forecasting has been developed from a human-intensive task [5] to a computational process [6], and to this end, it requires high-tech equipment. There are various factors that can afect the precision of forecasts. Season, geographical location, the accuracy of input data, classifcations of weather, lead time, and validity time are some of these efective factors [7, 8]. |