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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

Fayaz Sheikh Amir, Zaman Majid, Butt Muheet Ahmed, Kaul Sameer

Applied Computer Science

2022

Vol. 18, no 4

16--27

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.

The Feature Selection based Power Quality Event Classification using Wavelet Transform and Logistic Model Tree

Eristi H., Demir Y.

Przegląd Elektrotechniczny

2012

R. 88, nr 7a

43-48

This paper presents a new power quality event classification technique using wavelet transform and logistic model tree. The proposed method uses the samples of three cycle duration of three line voltage of power quality events. The features of these samples are obtained by using the wavelet transform and a few different feature extraction techniques. The sequential forward selection method based a feature selection process is done to ensure good classification accuracy by selecting 20 better features from all 90 features generated from the wavelet transform coefficients. The obtained features are used to train a single logistic model tree. The feasibility of the proposed algorithm has been tested using real life power quality events. The result indicates that the feature selection based proposed method reliably classifies all types of power quality events with high accuracy.

W artykule zaproponowano nową metodę oceny jakości energii wykorzystującą transformatę falkową i logistyczny model drzewa. W metodzie analizuje się trzy cykle w trzech liniach napięcia. Możliwa jest klasyfikacja 90 zdarzeń i wybranie 20 typowych cech.