Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis
In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/8/1233 |
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| author | Yinghui Meng Sultan Noman Qasem Manouchehr Shokri Shahab S |
| author_facet | Yinghui Meng Sultan Noman Qasem Manouchehr Shokri Shahab S |
| author_sort | Yinghui Meng |
| collection | DOAJ |
| description | In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers. |
| first_indexed | 2024-03-10T18:10:55Z |
| format | Article |
| id | doaj.art-3759e658de29449fbbbf500ec28ae2b8 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T18:10:55Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3759e658de29449fbbbf500ec28ae2b82023-11-20T08:03:57ZengMDPI AGMathematics2227-73902020-07-0188123310.3390/math8081233Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component AnalysisYinghui Meng0Sultan Noman Qasem1Manouchehr Shokri2Shahab S3School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, ChinaComputer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi ArabiaFaculty of civil engineering, Institute of Structural Mechanics (ISM), Bauhaus-Universität Weimar, 99423 Weimar, GermanyInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamIn this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful tool for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-wavelet-ANN models are compared with those obtained from wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicator in rivers.https://www.mdpi.com/2227-7390/8/8/1233machine learningdimensionality reductionwavelet transformwater qualityprincipal component analysis |
| spellingShingle | Yinghui Meng Sultan Noman Qasem Manouchehr Shokri Shahab S Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis Mathematics machine learning dimensionality reduction wavelet transform water quality principal component analysis |
| title | Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis |
| title_full | Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis |
| title_fullStr | Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis |
| title_full_unstemmed | Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis |
| title_short | Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis |
| title_sort | dimension reduction of machine learning based forecasting models employing principal component analysis |
| topic | machine learning dimensionality reduction wavelet transform water quality principal component analysis |
| url | https://www.mdpi.com/2227-7390/8/8/1233 |
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