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How machine learning algorithms are used in meteorological data classification: a comparative approach between DT, LMT, M5-MT, gradient boosting and GWLM-NARX models

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
16--27
Opis fizyczny
Bibliogr. 30 poz., fig., tab.
Twórcy
  • Department of Computer Sciences, University of Kashmir, J&K, India
autor
  • Directorate of IT & SS, University of Kashmir, J&K, India
  • Department of Computer Sciences, University of Kashmir, J&K, India
autor
  • Department of Computer Sciences, University of Kashmir, J&K, India
Bibliografia
  • [1] Adnan, R. M., Petroselli, A., Heddam, S., Santos, C. A. G., & Kisi, O. (2021). Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Natural Hazards, 105(3), 2987–3011.
  • [2] Afolayan, H. A., Ojokoh, B. A., & Falaki, S. O. (2016). Comparative analysis of rainfall prediction models using neural network and fuzzy logic. International Journal of Soft Computing and Engineering, 5(6), 4–7.
  • [3] Aftab, S., Ahmad, M., Hameed, N., Bashir, M. S., Ali, I., & Nawaz, Z. (2018). Rainfall prediction using data mining techniques: A systematic literature review. International journal of advanced computer science and applications, 9(5), 143–150.
  • [4] Aguasca-Colomo, R., Castellanos-Nieves, D., & Méndez, M. (2019). Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island. Applied Sciences, 9(22), 4931. https://doi.org/10.3390/app9224931
  • [5] Altaf, I., Butt, M. A., & Zaman, M. (2021). A Pragmatic Comparison of Supervised Machine Learning Classifiers for Disease Diagnosis. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 1515–1520). IEEE. https://doi.org/10.1109/ICIRCA51532.2021.9544582
  • [6] Banday, I.R., Zaman, M., Quadri, S.M.K., Fayaz, S.A., Butt, M.A. (2022). Big data in academia: A proposed framework for improving students performance. Revue d'Intelligence Artificielle, Vol. 36, No. 4, pp. 589–595. https://doi.org/10.18280/ria.360411
  • [7] Barrera–Animas, A. Y., Oyedele, L. O., Bilal, M., Akinosho, T. D., Delgado, J. M. D., & Akanbi, L. A. (2022). Rainfall prediction: A comparative analysis of modern machine learning algorithms for time-series forecasting. Machine Learning with Applications, 7, 100204. https://doi.org/10.1016/j.mlwa.2021.100204
  • [8] Dhamodaran, S., & Lakshmi, M. (2021). Comparative analysis of spatial interpolation with climatic changes using inverse distance method. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6725–6734. https://doi.org/10.1007/s12652-020-02296-1
  • [9] Fayaz, S. A., Kaul, S., Zaman, M., & Butt, M. A. (2022). An adaptive gradient boosting model for the prediction of rainfall using ID3 as a base estimator. Revue d'Intelligence Artificielle, 36(2), 241–250. https://doi.org/10.18280/ria.360208
  • [10] Fayaz, S. A., Zaman, M., & Butt, M. A. (2021a). To ameliorate classification accuracy using ensemble distributed decision tree (DDT) vote approach: An empirical discourse of geographical data mining. Procedia Computer Science, 184, 935–940. https://doi.org/10.1016/j.procs.2021.03.116
  • [11] Fayaz, S. A., Zaman, M., & Butt, M. A. (2021b). An application of logistic model tree (LMT) algorithm to ameliorate Prediction accuracy of meteorological data. International Journal of Advanced Technology and Engineering Exploration, 8(84), 1424–40.
  • [12] Fayaz, S. A., Zaman, M., & Butt, M. A. (2021c). A hybrid adaptive grey wolf Levenberg-Marquardt (GWLM) and nonlinear autoregressive with exogenous input (NARX) neural network model for the prediction of rainfall. International Journal of Advanced Technology and Engineering Exploration, 9(89), 509–522. https://doi.org/10.19101/IJATEE.2021.874647
  • [13] Fayaz, S. A., Zaman, M., & Butt, M. A. (2022a). Numerical and Experimental Investigation of Meteorological Data Using Adaptive Linear M5 Model Tree for the Prediction of Rainfall. Review of Computer Engineering Research, 9(1), 1–12.
  • [14] Fayaz, S. A., Zaman, M., & Butt, M. A. (2022b). Knowledge Discovery in Geographical Sciences—A Systematic Survey of Various Machine Learning Algorithms for Rainfall Prediction. In International Conference on Innovative Computing and Communications (pp. 593–608). Springer.
  • [15] Fayaz, S. A., Zaman, M., & Butt, M. A. (2022c). Performance Evaluation of GINI Index and Information Gain Criteria on Geographical Data: An Empirical Study Based on JAVA and Python. In International Conference on Innovative Computing and Communications (pp. 249–265). Springer.
  • [16] Fayaz, S. A., Zaman, M., Kaul, S., & Butt, M. A. (2022). Is Deep Learning on Tabular Data Enough? An Assessment. International Journal of Advanced Computer Science and Applications, 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130454
  • [17] Kaul, S., Fayaz, S. A., Zaman, M., & Butt, M. A. (2022). Is decision tree obsolete in its original form? A burning debate. Revue d'Intelligence Artificielle, 36(1), 105–113.
  • [18] Kaul, S., Zaman, M., Fayaz, S. A., & Butt, M. A. (2023). Performance Stagnation of Meteorological Data of Kashmir. In International Conference on Innovative Computing and Communications. Lecture Notes in Networks and Systems (vol. 471). Springer. https://doi.org/10.1007/978-981-19-2535-1_63
  • [19] Mohd, R., Butt, M. A., & Baba, M. Z. (2020). GWLM–NARX: grey wolf levenberg–marquardt-based neural network for rainfall prediction. Data Technologies and Applications, 54(1), 85–102. https://doi.org/10.1108/DTA-08-2019-0130. 2020.
  • [20] Mohd, R., Butt, M. A., & Baba, M. Z. (2022). Grey Wolf-Based Linear Regression Model for Rainfall Prediction. International Journal of Information Technologies and Systems Approach, 15(1), 1-18.
  • [21] Niu, J., & Zhang, W. (2015). Comparative analysis of statistical models in rainfall prediction. In 2015 IEEE International Conference on Information and Automation (pp. 2187-2190). IEEE.
  • [22] Pucheta, J. A., Cristian, M. R. R., Martín, R. H., Carlos, A. S., Patiño, H. D., & Benjamín, R. K. (2009). A feed-forward neural networks-based nonlinear autoregressive model for forecasting time series. Comput y Sistemas, 14(4), 423–435.
  • [23] Rezaie-balf, M., Naganna, S. R., Ghaemi, A., & Deka, P. C. (2017). Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. Journal of hydrology, 553, 356–373.
  • [24] Singh, P., & Borah, B. (2013). Indian summer monsoon rainfall prediction using artificial neural network. Stochastic Environmental Research and Risk Assessment, 27(7), 1585–1599.
  • [25] Singh, U., Chauhan, S., Krishnamachari, A., & Vig, L. (2015). Ensemble of deep long short term memory networks for labelling origin of replication sequences. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) (pp.1–7). IEEE. http://dx.doi.org/10.1109/DSAA.2015.7344871
  • [26] Wu, C., & Chau, K.-W. (2013). Prediction of rainfall time series using modular soft computing methods. Engineering Applications of Artificial Intelligence, 26(3), 997–1007. https://doi.org/10.1016/j.engappai.2012.05.023
  • [27] Xiang, Y., Gou, L., He, L., Xia, S., & Wang, W. (2018). A SVR–ANN combined model based on ensemble EMD for rainfall prediction. Applied Soft Computing, 73, 874–883. https://doi.org/10.1016/j.asoc.2018.09.018
  • [28] Yang, Y., Lin, H., Guo, Z., & Jiang, J. (2007). A data mining approach for heavy rainfall forecasting based on satellite image sequence analysis. Comput Geosci, 33(1), 20–30.
  • [29] Zaman, M., & Butt, M. A. (2012). Information translation: a practitioners approach. In World Congress on Engineering and Computer Science (WCECS).
  • [30] Zaz, S. N., Romshoo, S. A., Krishnamoorthy, R. T., & Viswanadhapalli, Y. (2019). Analyses of temperature and precipitation in the Indian Jammu and Kashmir region for the 1980–2016 period: implications for remote influence and extreme events. Atmospheric Chemistry and Physics, 19(1), 15-37. https://doi.org/10.5194/acp-19-15-2019
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-accc03e4-4b2e-436d-8352-dc88c1e07892
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