| تعداد نشریات | 63 |
| تعداد شمارهها | 2,257 |
| تعداد مقالات | 18,555 |
| تعداد مشاهده مقاله | 57,400,909 |
| تعداد دریافت فایل اصل مقاله | 29,417,377 |
Applying the Modified Sinc Neural Network for Weather Forecasting | ||
| Journal of Data Science and Modeling | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 26 تیر 1404 اصل مقاله (426.7 K) | ||
| نوع مقاله: Research Manuscript | ||
| شناسه دیجیتال (DOI): 10.22054/jdsm.2025.84908.1065 | ||
| نویسنده | ||
| Ghasem Ahmadi* | ||
| Department of Mathematics, Payame Noor University, Tehran, Iran. | ||
| چکیده | ||
| Accurate weather prediction plays a vital role in many sectors, such as agriculture, disaster preparedness, transportation systems, and urban planning. Traditional meteorological models face challenges in capturing complex atmospheric dynamics, leading to increased reliance on artificial neural networks (ANNs) for improved forecasting accuracy. ANNs have been widely applied in meteorology due to their ability to model nonlinear relationships and temporal dependencies. Based on the Sinc numerical methods, the modified Sinc neural network (MSNN) has been introduced recently. This model uses the advantages of the Sinc function, such as smoothness and fluctuation, and at the same time improves the ability to model nonlinear dependencies and temporal dynamics in environmental data. This work utilizes the MSNN for time series forecasting where its parameters are adjusted with a discrete-time online Lyapunov-based learning algorithm. Then, it is applied to enhance the weather forecasting. This model is evaluated on datasets containing various meteorological variables. The data used in this article is related to the city of Khorramabad in Iran. The results show that despite its simple structure, MSNN has a high efficiency in weather forecasting. | ||
| کلیدواژهها | ||
| Weather forecasting؛ time series forecasting؛ Sinc neural network | ||
|
آمار تعداد مشاهده مقاله: 391 تعداد دریافت فایل اصل مقاله: 148 |
||