HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS
Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate pr...
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| Format: | Article |
| Language: | English |
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Polish Association for Knowledge Promotion
2022-12-01
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| Series: | Applied Computer Science |
| Subjects: | |
| Online Access: | http://www.acs.pollub.pl/pdf/v18n4/2.pdf |
| _version_ | 1797955754040229888 |
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| author | Sheikh Amir FAYAZ Majid ZAMAN Muheet Ahmed BUTT Sameer KAUL |
| author_facet | Sheikh Amir FAYAZ Majid ZAMAN Muheet Ahmed BUTT Sameer KAUL |
| author_sort | Sheikh Amir FAYAZ |
| collection | DOAJ |
| description | Rainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing. |
| first_indexed | 2024-04-10T23:39:05Z |
| format | Article |
| id | doaj.art-2ee692d06e004c79baafca757a2f3744 |
| institution | Directory Open Access Journal |
| issn | 1895-3735 2353-6977 |
| language | English |
| last_indexed | 2024-04-10T23:39:05Z |
| publishDate | 2022-12-01 |
| publisher | Polish Association for Knowledge Promotion |
| record_format | Article |
| series | Applied Computer Science |
| spelling | doaj.art-2ee692d06e004c79baafca757a2f37442023-01-11T13:11:14ZengPolish Association for Knowledge PromotionApplied Computer Science1895-37352353-69772022-12-01184162710.35784/acs-2022-26HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELSSheikh Amir FAYAZ0https://orcid.org/0000-0001-6606-0864Majid ZAMAN1https://orcid.org/0000-0003-1070-8195Muheet Ahmed BUTT2https://orcid.org/0000-0002-8059-0180Sameer KAUL3https://orcid.org/0000-0003-0911-0073Department of Computer Sciences, University of Kashmir, J&K, India, skh.amir88@gmail.comDirectorate of IT & SS, University of Kashmir, J&K, India, zamanmajid@gmail.comDepartment of Computer Sciences, University of Kashmir, J&K, IndiaDepartment of Computer Sciences, University of Kashmir, J&K, IndiaRainfall prediction is one of the most challenging task faced by researchers over the years. Many machine learning and AI based algorithms have been implemented on different datasets for better prediction purposes, but there is not a single solution which perfectly predicts the rainfall. Accurate prediction still remains a question to researchers. We offer a machine learning-based comparison evaluation of rainfall models for Kashmir province. Both local geographic features and the time horizon has influence on weather forecasting. Decision trees, Logistic Model Trees (LMT), and M5 model trees are examples of predictive models based on algorithms. GWLM-NARX, Gradient Boosting, and other techniques were investigated. Weather predictors measured from three major meteorological stations in the Kashmir area of the UT of J&K, India, were utilized in the models. We compared the proposed models based on their accuracy, kappa, interpretability, and other statistics, as well as the significance of the predictors utilized. On the original dataset, the DT model delivers an accuracy of 80.12 percent, followed by the LMT and Gradient boosting models, which produce accuracy of 87.23 percent and 87.51 percent, respectively. Furthermore, when continuous data was used in the M5-MT and GWLM-NARX models, the NARX model performed better, with mean squared error (MSE) and regression value (R) predictions of 3.12 percent and 0.9899 percent in training, 0.144 percent and 0.9936 percent in validation, and 0.311 percent and 0.9988 percent in testing.http://www.acs.pollub.pl/pdf/v18n4/2.pdfmeteorological datam5 model treelinear model functionsgradient boostinglogistic model trees |
| spellingShingle | Sheikh Amir FAYAZ Majid ZAMAN Muheet Ahmed BUTT Sameer KAUL HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS Applied Computer Science meteorological data m5 model tree linear model functions gradient boosting logistic model trees |
| title | HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS |
| title_full | HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS |
| title_fullStr | HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS |
| title_full_unstemmed | HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS |
| title_short | HOW MACHINE LEARNING ALGORITHMS ARE USED IN METEOROLOGICAL DATA CLASSIFICATION: A COMPARATIVE APPROACH BETWEEN DT, LMT, M5-MT, GRADIENT BOOSTING AND GWLM-NARX MODELS |
| title_sort | how machine learning algorithms are used in meteorological data classification a comparative approach between dt lmt m5 mt gradient boosting and gwlm narx models |
| topic | meteorological data m5 model tree linear model functions gradient boosting logistic model trees |
| url | http://www.acs.pollub.pl/pdf/v18n4/2.pdf |
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