Evaluating Imputation Approaches and Support Vector Regression Parameters in Weather Forecasting

Arif Mudi Priyatno; Yunia Ningsih

doi:10.69693/jesa.v2i2.34

Authors

Arif Mudi Priyatno University of Pahlawan Tuanku Tambusai
Yunia Ningsih Universitas Trisakti

DOI:

https://doi.org/10.69693/jesa.v2i2.34

Keywords:

Rainfall; Data Imputation; Support Vector Regression; K-Nearest Neighbor; Radial Basis Function Kernel; Artificial Neural Network.

Abstract

Rainfall plays a vital role in various sectors such as transportation, agriculture, and industry. Having accurate rainfall information enables stakeholders in these fields to take proper measures and minimize potential losses caused by inaccurate data. This study focuses on identifying an effective method for rainfall forecasting by examining imputation techniques in data preprocessing and parameter settings within Support Vector Regression (SVR). The experimental findings indicate that the most effective imputation method for SVR is determined using the Mean Squared Error (MSE) and Mean Absolute Error (MAE) evaluation metrics. Based on MSE, the k-nearest neighbor method proves to be the most reliable approach for data imputation preprocessing. The preprocessing results were then applied to Polynomial SVR with parameters C = 1000, tolerance = 0.001, epsilon = 0.01, and unlimited iterations. Conversely, MAE results highlight Artificial Neural Network (ANN) as the optimal imputation method. ANN, when combined with a radial basis function kernel, gamma = 0.001, C = 1000, tolerance = 0.001, and unlimited iterations, was further tested using RBF SVR under the same parameter settings.

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