deep learning for big weather data analyzing and forecasting

Weather prediction is vital in daily life routines, for risk mitigation and resource management such as floodrisk 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 systemor a model to forecast the highly nonlinear changing happening in the climate is required. the focus of this researchis direct prediction of forecasting from weather-changing parameters, the forecasts are performed using collected datavalues 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 proposinga deep learning approach (cnn). the research goal is predication the weather condition. the acquired classificationaccuracy for the climate condition using cnn (shufflenet) structure is 98%, and the recall and precision results are 97.5and 96.9 respectively