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https://doi.org/10.20532/cit.2016.1002715

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Sudha Mohankumar ; Information Technology Department, School of Information Technology and Engineering, VIT University, Vellore, India
Valarmathi Balasubramanian ; Software and Systems Engineering Department, School of Information Technology and Engineering, VIT University, Vellore, India

Puni tekst: engleski, pdf (621 KB) str. 181-194 preuzimanja: 244* citiraj
APA 6th Edition
Mohankumar, S. i Balasubramanian, V. (2016). Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique. Journal of computing and information technology, 24 (2), 181-194. https://doi.org/10.20532/cit.2016.1002715
MLA 8th Edition
Mohankumar, Sudha i Valarmathi Balasubramanian. "Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique." Journal of computing and information technology, vol. 24, br. 2, 2016, str. 181-194. https://doi.org/10.20532/cit.2016.1002715. Citirano 27.02.2020.
Chicago 17th Edition
Mohankumar, Sudha i Valarmathi Balasubramanian. "Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique." Journal of computing and information technology 24, br. 2 (2016): 181-194. https://doi.org/10.20532/cit.2016.1002715
Harvard
Mohankumar, S., i Balasubramanian, V. (2016). 'Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique', Journal of computing and information technology, 24(2), str. 181-194. https://doi.org/10.20532/cit.2016.1002715
Vancouver
Mohankumar S, Balasubramanian V. Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique. Journal of computing and information technology [Internet]. 2016 [pristupljeno 27.02.2020.];24(2):181-194. https://doi.org/10.20532/cit.2016.1002715
IEEE
S. Mohankumar i V. Balasubramanian, "Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique", Journal of computing and information technology, vol.24, br. 2, str. 181-194, 2016. [Online]. https://doi.org/10.20532/cit.2016.1002715

Sažetak
Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naïve Bayes (NB), Bayesian Logistic Regression (BLR), Multi-Layer Perceptron (MLP), J48, Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM), to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction.

Ključne riječi
rainfall prediction; rough set; maximum frequency; optimal reduct; core features and accuracy

Hrčak ID: 161746

URI
https://hrcak.srce.hr/161746

Posjeta: 402 *