Abstract:
هدف از این پژوهش مقایسة عملکرد مدلهای سری زمانی SARIMA و Holt-Winters با روشهای هوش مصنوعی شامل شبکة عصبی مبتنی بر توابع پایة شعاعی (RBF) و سیستم استنباط عصبی- فازی تطبیقی (ANFIS) بهمنظور پیشبینی فراوانی روزهای همراه با طوفان گردوغبار (FDSD) در فصل آتی است. بدین منظور، از دادههای ساعتی گردوغبار و کدهای سازمان جهانی هواشناسی در پنج ایستگاه سینوپتیک استان سیستان و بلوچستان با طول دورة آماری 25ساله (۱۹۹۰-2014) استفاده شد. نتایج نشان داد روش ANFIS، نسبت به سایر روشها، بهترین عملکرد را داشت و معیارهای ارزیابی R، RMSE، MAE، و NS آن بهترتیب از 72/0، 57/0، 42/0، و 71/0 تا 95/0، 51/0، 40/0، و 96/0 متغیر بود. همچنین، با افزایش شاخص متوسط FDSD در ایستگاهها (از 06/1 تا 11/7)، دقت پیشبینی همة روشها افزایش داشت. بر همین اساس، در سری زمانی SARIMA، ضریب همبستگی بین مقادیر مشاهداتی و پیشبینیشده شاخص FDSD از 64/0 به 79/0 افزایش یافت. برای روشهای Holt-Winters، RBF، و ANFIS مقدار نیز ضریب همبستگی بهترتیب از 70/0 تا 87/0، 69/0 تا 92/0، و 72/0 تا 95/0 متغیر بود. درمجموع، با مقایسة روشهای مورد استفاده، روش هوش مصنوعی ANFIS بهترین و مدلهای سری زمانی SARIMA و Holt-Winters بدترین عملکرد را داشتند.
Introduction: The impact of the dust phenomenon in Iran is so great that it has involved more than half of the country's provinces in some way with the problems and limitations of this natural phenomenon, which in addition to environmental impacts, has disrupted the implementation of sustainable national development plans and so far it has had and will have many negative consequences. The increase in dust storms in recent years in the east and southeast of the country, especially in Sistan and Baluchestan province, and consequently the decrease in air quality in these areas, has doubled the importance of Forecasting this phenomenon. On the other hand, most domestic studies in this field are related to the process of small-scale dust phenomena, synoptic studies, and its satellites. Therefore, considering that this phenomenon has had adverse effects and negative consequences in the social, economic, and health fields of the people, it is necessary to study, forecast, and measure its relationship with climate variations. Materials and Methods: This study aimed to compare the performance of SARIMA and Holt-Winters time series models with artificial intelligence methods including neural networks based on radial base functions (RBF) and adaptive neural-fuzzy inference system (ANFIS) to forecast the frequency of dust storm days (FDSD) in the next season. For this purpose, hourly dust data and codes of the World Meteorological Organization were used in five synoptic stations in Sistan and Baluchestan province with a statistical period of 25 years (1990-2014). The observations of meteorological phenomena are recorded once every three hours, a total of eight times a day. In these observations, the visual phenomena of climate are defined according to the guidelines of the World Meteorological Organization in 100 codes (00-99), in which 11 codes are used to record and report the phenomenon of dust in different meteorological stations. Following the time series of days with dust storms, the FDSD index was forecasted using four methods SARIMA, Holt-Winters, RBF, and ANFIS. Results and Discussion: According to the results of the time series, the FDSD index in Saravan, Khash, Iranshahr, and Zahedan stations has relatively small variations that are scattered throughout the time series, but with the increase in the number of dust days in Zabol station, the scattering of the variations has decreased and its intensity has increased. Also, the peak values of dust are concentrated next to each other, which indicates the occurrence of successive dust storms in this station from 2000 onwards. As can be seen in the ACF and PACF diagrams of the studied stations, significant time intervals indicate the correlation between the time values that make it possible to modeling and forecasting future values (next season) of the FDSD index for all five stations studied. According to the functions of partial autocorrelation and autocorrelation, the range of change of attraction and the moving average was determined, and using the appropriate evaluation criteria, the best time series model was extracted for each station. In the Dickey-Fuller test, the significance level was considered to be P-Value Conclusion: The results showed that with the decrease in the frequency of days with dust storms in Saravan and Khash stations, the Holt-Winters time series model showed almost the same and higher performance than the RBF method, which indicated the high capability of this model to forecast low values FDSD index. The results also showed that the SARIMA time series model compared to other forecasting methods did not have a high ability in forecasting the FDSD index in any of the studied stations. Also, despite the low frequency of days with dust storms in Iranshahr station compared to Zahedan station, all FDSD index forecasting methods have better performance and more accurately than Zahedan station based on evaluation criteria, which can be searched due to the presence of a complete series without FDSD index termination at Iranshahr station. The results of this study can be useful in forecasting and managing the consequences of dust storms in the study areas. On the other hand, in forecasting the FDSD index in Sistan and Baluchestan province, the optimal predictor model has been complex. For all of the stations studied, the model that used three or four steps of the predictive delay was recognized as the best predictor model. Therefore, particles leftover from previous storms could be an important reason for the impact of the last few seasons’ storms on the formation of dust storms in future seasons.
Machine summary:
در اين مطالعه به مقايسۀ عملکرد مدل هاي سري زماني SARIMA و Holt-Winters با روش هاي هوش مصنوعي RBF و ANFIS به منظور پيش بيني فراواني روزهاي همراه با طوفان گردوغبار (FDSD)١ پرداخته شده 1.
موادوروش ها منطقۀموردمطالعه وداده هايمورداستفاده در اين پژوهش به بررسي مقايسۀ عملکرد روش هاي آماري کلاسيک (SARIMA و Holt-Winters) و مدل هاي هوش مصنوعي (RBF و ANFIS) به منظور پيش بيني فراواني روزهاي همراه با طوفان گردوغبار (FDSD) در پنج ايستگاه سينوپتيک استان سيستان و بلوچستان (زابل ، زاهدان ، ايرانشهر، خاش ، و سراوان )، که بيشترين فراواني روزهاي همراه با طوفان هاي گردوغبار در کشور را شامل ميشوند (عراقينژاد و همکاران ، ١٣٩٧: ٢١)، با جامعۀ آماري ٢٥ساله (١٩٩٠- ٢٠١٤) در مقياس فصلي پرداخته شد.
World Meteorological Organization پس از انتخاب ايستگاه ها و بررسي داده ها در بازة زماني ٢٥ساله (١٩٩٠-٢٠١٤)، تعداد روزهاي همراه با طوفان گردوغبار (FDSD) براي پنج ايستگاه هواشناسي مورد مطالعه در استان سيستان و بلوچستان با استفاده از داده هاي ديد افقي و کدهاي سازمان هواشناسي محاسبه و در جدول ٢ نشان داده شده است .
اين شکل بهبود عملکرد مدل هاي مورد استفاده به منظور پيش بيني فراواني روزهاي همراه با طوفان گردوغبار را با به کارگيري روش ANFIS در همۀ ايستگاه ها به خوبي بيان ميکند.
با کاهش فراواني روزهاي همراه با طوفان گردوغبار در ايستگاه هاي سراوان و خاش ، مدل سري زماني Holt-Winters به ترتيب عملکرد تقريبا يکسان و بالاتري نسبت به روش RBF از خود نشان داد که حاکي از قابليت بالاي اين مدل به منظور پيش بيني مقادير کم شاخص FDSD بود.