Weather Prediction Based on the Cloud Images Using Machine and Deep Learning Algorithms

The process of weather forecasting based on the climatic images captured at various geographical locations and times can guide humans to plan their daily routines. In this paper, we propose forecasting the weather based on the images captured from the sky. The image dataset collected from various online search engines and mobile cameras pertains to timings such as sunrise, sunset, daytime, and night. Care has been taken to consider various weather conditions such as clear, foggy, rainy, and glare. Machine learning (ML) is widely adapted for various real-time applications. The inception of deep learning (DL) architectures has already been proven to outperform the traditional feature engineering approaches. This paper proposes two approaches. One is using the traditional ML models and the other is using trending convolutional neural networks (CNNs). In ML-based approach, decision tree (DT) and random forest (RF) are considered. Manually extracted features such as brightness, sharpness, contrast, white pixel intensity, haze factor, and color histogram are fed to them. Meanwhile, in the CNN-based approach, VGG16 and RESNET architectures are used. The performance of CNNs is found to be better when compared to the traditional ML algorithms. The basic CNN, VGG16, and RESNET models provide an accuracy of 62%, 72%, and 73.35%, respectively.