Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3022
Title: Support Vector Machine Based an Efficient and Accurate Seasonal Weather Forecasting Approach with Minimal Data Quantities
Authors: Chandrasekara, S
Tennekoon, S
Abhayasinghe, N
Seneviratne, L
Keywords: Support Vector Machines
Principal Component Analysis
Receiver Operating Characteristic
Machine Learning
Weather Forecast
Hyperparameter Optimization
Issue Date: 11-Feb-2022
Publisher: SLIIT
Series/Report no.: Proceedings of the SLIIT International Conference On Engineering and Technology,;Vol. 01
Abstract: Climate change makes a big impact in our daily activities. Therefore, forecasting climate changes prior to its actual occurrences is important. Even though highly accurate weather prediction systems throughout the world are available, they require mass amounts of data exceeding thousands of data points to obtain a significant accuracy. This study was aimed at proposing a Support Vector Machine based approach to carryout seasonal weather predictions up to thirty-minute intervals, the results of which would be considerably effective with respect to predictions carried out with models trained with annual datasets. The model was trained utilizing a dataset corresponding to the district of Kandy which consisted of 136 samples, 20 features, and 5 labels. By means of carrying out numerous data preprocessing steps, the model was trained, and the relevant hyperparameters were optimized considering the grid search algorithm to yield a maximum accuracy of 86%, once tested via the k-fold cross validation. The performance of the Support Vector Machine was also then compared for the same dataset with that of the K-Nearest Neighbor algorithm which consumed relatively fewer computing resources. An optimal accuracy of 61% was observed for this model for a K-value of 27. This approach supported the concept of a Support Vector Machine’s ability to perceive time series forecasts to a relatively higher degree and its ability to perform effectively in higher dimensional datasets with smaller number of samples. As per the future work, the Receiver Operating Characteristic analysis is proposed to be carried out to evaluate the performance of the model and the dataset size is proposed to be further enhanced to a maximum of a thousand samples to yield the best performance results.
URI: http://rda.sliit.lk/handle/123456789/3022
ISSN: 2961-5011
Appears in Collections:Proceedings of the SLIIT International Conference On Engineering and Technology Vol. 01(SICET) 2022
Research Papers - Department of Electrical and Electronic Engineering

Files in This Item:
File Description SizeFormat 
Draft 7(335-345).pdf957.17 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.