| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
