Contents I. Introduction
Weather is a crucial aspect of our daily lives, affecting a wide range of activities and industries such as transportation, agriculture, and energy management. Transportation systems, for example, can be disrupted by adverse weather conditions, leading to traffic congestion, delays, and accidents [1]. Agriculture is also heavily influenced by weather conditions, with farmers needing to make decisions based on weather forecasts to ensure that their crops are adequately irrigated and protected from extreme weather events. Similarly, energy management systems rely on accurate weather forecasts to optimize energy production and distribution, which can affect the reliability and cost of energy supply [1]. Accurately detecting and classifying weather conditions is, therefore, of great importance for a variety of applications. Traditional methods of weather monitoring, such as meteorological instruments, have limitations in terms of their cost, spatial and temporal coverage, and accuracy. These instruments require specialized training to operate, and the data they provide is often limited to specific locations and times [2]. For instance, anemometers can measure wind speed at a specific location, but they do not provide information on wind direction or other weather parameters such as precipitation and temperature. In contrast, image-based weather detection systems can overcome some of the limitations of traditional methods by providing a more comprehensive view of the weather conditions. By analyzing images from multiple sources and locations, we can create a more accurate and robust picture of the current weather conditions [3]. In addition, image-based systems can provide real-time data, allowing for more rapid responses to extreme weather events. However, there are challenges associated with using image-based systems for weather detection, such as the need for large amounts of data for training DL models, the complexity of the models, and the potential for overfitting. Recent advances in computer vision and machine learning have opened up new possibilities for weather detection using image processing techniques [4] [5] [6]. In particular, DL models such as CNN have shown impressive performance in detecting and classifying weather conditions from images [7]. However, there are challenges associated with using CNNs for weather detection, such as the need for large amounts of data for training, the complexity of the models, and the potential for overfitting. To overcome some of these challenges, we propose an amalgamated DL model of CNNs and SVM for weather detection and multi-classification. Our model is trained on a dataset of 10,000 self-collected images, which includes five different weather conditions: sunny, rainy, windy, snowing, and cloudy. Our approach involves pre-processing the images by resizing them to a standard size and normalizing the pixel values. We then use transfer learning with the VGG16 architecture to train a CNN on the pre-processed images. The trained CNN is used to extract features from the images, which are then fed into an SVM classifier to perform multi-class classification. The main contribution of our research is the development of a novel approach to weather detection and multi-classification using an amalgamated DL model of CNNs and SVMs. Our approach is based on the observation that CNNs are effective in extracting features from images, while SVMs are well-suited for multi-class classification tasks. By combining these two models, we create a more accurate and robust weather detection system. Pre-processing the photographs by shrinking them to a standard size and normalising the pixel values is an integral part of the strategy that we have presented. We then use transfer learning with the VGG16 architecture to train a CNN on the pre-processed images. The trained CNN is used to extract features from the images, which are then fed into an SVM classifier to perform multi-class classification. Our approach has several advantages over existing methods of weather detection using models. Firstly, our model is more accurate and robust because it combines the strengths of both CNNs and SVMs. CNNs are effective in detecting spatial patterns in images, while SVMs are well-suited for multi-class classification tasks. Secondly, our approach reduces the risk of overfitting because the CNN is trained on pre-processed images, and transfer learning is used to leverage pre-trained weights. Finally, our approach is more efficient because the SVM classifier uses the extracted features from the CNN, reducing the computational burden of training the entire model end-to-end. In this research paper, we provide a comprehensive review of the literature on weather detection using image processing techniques. We describe our methodology in detail, including the dataset, pre-processing steps, and the DL model. We also present experimental results that demonstrate the effectiveness of our approach in detecting and classifying weather conditions. In a nutshell we address the ramifications of our findings and offer possible directions for further investigation.
