Abstract
Nowadays, automation is at its peak. The article provides a base to examine the weather through the machine without human intervention. This study offers a thorough classification model to forecast a weather type. Here, the model facilitates defining the best results for the weather prediction model to any climatic zones and categorizes the climate into four types: cloud, rain, shine, and sunrise. This model designs and reveals using convolution neural networks (CNN) algorithms with Keras framework and TensorFlow library. For practical implementations, use the images dataset available from the kaggle.com website. As a result, this research presents the performance of the designed and developed model. It shows accuracy, validation accuracy, losses, and validation losses approximately 94%, 92%, 18%, and 22%, respectively.
Highlights
This article represents the weather classifications model without human intervention
The results presented with higher accuracy and climatic conditions are suitable for vegetation, spices [2, 4]
The initial phase prepares the image dataset for further processing with the implementing model
Summary
This article represents the weather classifications model without human intervention. The paper introduces an intensified climate foresight model that employs the notion of data mining in weather prediction. The model can divine any characteristic feasible in the dataset while other data mining-based algorithms produce particular traits. It is computationally valuable and executes appropriately to mobile devices like the Android ecosystem [1]. It uncovers the distinct climatic conditions and separates the cloudy, rainy, shiny, and foggy patterns. The genetic classifications comprise based on the geographic determinants of the environment and the surface energy resources, plus concerns of the air-mass review [2, 6]
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