Data mining techniques for atmospheric variables

There is a plethora of tools available in the literature for analysis and prediction. Some of them are built on top of JAVA scripts for Matlab, Minitab, SPSS, R, Ky plot, etc., while others are based on C programming. The article makes use of the WEKA programme, which stands for Waikato Environment...

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Bibliographic Details
Main Authors: Reddy, S. Venkatramana, Harish, K., Reddy, N. Konda, Sarojamma, B.
Format: Conference Proceeding
Language:English
Subjects:
Data mining
Wind speed
Online Access:Get full text
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Summary:There is a plethora of tools available in the literature for analysis and prediction. Some of them are built on top of JAVA scripts for Matlab, Minitab, SPSS, R, Ky plot, etc., while others are based on C programming. The article makes use of the WEKA programme, which stands for Waikato Environment for Knowledge Analysis. We use atmospheric data such as visibility, wind speed, minimum and maximum temperatures, and local weighted learning (LWL) and regression by discretization (R2D) techniques based on M5P trees. After calculating the RMSE and SMAPE for each of the three trees, we can then pick the optimal one.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0212098