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Deep learning for big weather data analyzing and forecasting | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 8، دوره 15، شماره 2، اردیبهشت 2024، صفحه 87-94 اصل مقاله (521.36 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.30013.4313 | ||
| نویسندگان | ||
| Delveen L. Abd Al-Nabi* 1؛ Shereen Sh. Ahmed2 | ||
| 1Department of Economics, College of Economics and Administration, Duhok University, Duhok, Kurdistan Region, Iraq | ||
| 2Department of Computer Science, Faculty of Science, Zakho University, Duhok, Kurdistan Region, Iraq | ||
| تاریخ دریافت: 14 دی 1401، تاریخ بازنگری: 01 اسفند 1401، تاریخ پذیرش: 20 اسفند 1401 | ||
| چکیده | ||
| Weather prediction is vital in daily life routines, for risk mitigation and resource management such as flood risk forecasting. Quantitative prediction of weather changes depends on different parameters such as rainfall time, temporal, barometric pressure, humidity, precipitation, solar radiation and wind. Therefore, a highly accurate system or a model to forecast the highly nonlinear changing happening in the climate is required. The focus of this research is direct prediction of forecasting from weather-changing parameters, the forecasts are performed using collected data values recorded in a big dataset (the dataset collects the weather parameter data of the Canary Islands (Las Palmas, Tenerife a Palma, Fuerteventura, La Gomera, Lanzarote and Hierro). The forecasting system is performed by proposing a deep learning approach (CNN). The research goal is predication the weather condition. The acquired classification accuracy for the climate condition using CNN (ShuffleNet) structure is 98%, and the recall and Precision results are 97.5 and 96.9 respectively | ||
| کلیدواژهها | ||
| Deep learning؛ big data analysis؛ weather predication؛ machine learning | ||
| مراجع | ||
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