{"title":"基于机器智能方法的天气图像分类与分析","authors":"K. K. Jena, K. K","doi":"10.47992/ijaeml.2581.7000.0146","DOIUrl":null,"url":null,"abstract":"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. \nApproach: 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. \nResult: 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. \nOriginality: 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. \nPaper Type: Conceptual Research.","PeriodicalId":184829,"journal":{"name":"International Journal of Applied Engineering and Management Letters","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classification and Analysis of Weather Images Using Machine Intelligent Based Approach\",\"authors\":\"K. K. Jena, K. K\",\"doi\":\"10.47992/ijaeml.2581.7000.0146\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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. \\nApproach: 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. \\nResult: 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. \\nOriginality: 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. 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引用次数: 0
摘要
目的:天气信息在人类社会中起着至关重要的作用。它有助于减少与天气有关的损失,提高社会效益,如保护生命、健康、财产等。将天气图像(WIs)正确分类为露水、雾霾、霜、釉、冰雹、闪电、雨、彩虹、雾凇、沙尘暴、雪等,以便为人们和组织提供适当的信息进行进一步分析,这是非常必要的。方法:在这项工作中,提出了一种基于机器智能(MI)的方法,将WIs分类为露水、雾霾、霜、釉、冰雹、闪电、雨、彩虹、白霜、沙尘暴和雪类型。本文提出的方法主要是利用Logistic回归(LRG)、支持向量机(SVMN)、随机森林(RFS)和神经网络(NNT)方法的叠加(杂交)来进行分类。将该方法与LRG、SVMN、RFS、NNT、Decision Tree (DTR)、AdaBoost (ADB)、Naïve贝叶斯(NBY)、k -最近邻(KNNH)和随机梯度下降(SGDC)等其他基于机器学习(ML)的方法进行性能分析。结果:所提出的方法和其他基于ML的方法已经使用基于Python的Orange 3.26.0实现。在本工作中,从Kaggle源中分别提取了1449、1441、1446、150、1444、1446、142、147、149、147、143个露珠、雾霾、霜、釉、冰雹、闪电、雨、彩虹、雾凇、沙尘暴和雪类型的1604个WIs。使用分类精度(CA)、F1、精密度(PR)和召回率(RC)等性能参数对所有方法的性能进行了评估。从结果来看,与LRG、SVMN、RFS、NNT、DTR、ADB、NBY、KNNH和SGD等基于ML的方法相比,本文方法在CA、F1、PR和RC方面具有更好的分类效果。独创性:本文通过LRG、SVMN、RFS和NNT方法的叠加,提出了一种基于MI的方法,将WIs分类为露水、雾霾、霜、釉、冰雹、闪电、雨、彩虹、雾凇、沙尘暴、雪等几种类型。与LRG、SVMN、RFS、NNT、DTR、ADB、NBY、KNNH和SGDC方法相比,该方法在CA、F1、PR和RC方面表现更好。论文类型:概念研究。
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.