Classification and Analysis of Weather Images Using Machine Intelligent Based Approach

Purpose: Weather information plays a crucial role in the human society. It helps to lower the weather related losses and enhance the societal benefits such as the protection of life, health, property, etc., It is very much essential for the proper classification of weather images (WIs) into several categories such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, snow, etc. so that appropriate information can be provided to the people as well as organizations for further analysis.   Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of WIs into the dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1604 WIs having 149, 141, 146, 150, 144, 146, 142, 147, 149, 147, 143 numbers of dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow types respectively are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD.    Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of WIs into several types such as dew, fogsmog, frost, glaze, hail, lightning, rain, rainbow, rime, sandstorm, and snow type. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods.   Paper Type: Conceptual Research