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 time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The modelsIn 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.show moreshow less

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Metadaten
Document Type:Article
Author: Yinghui Meng, Sultan Noman QasemORCiD, Manouchehr Shokri, Shahab ShamshirbandORCiD
DOI (Cite-Link):https://doi.org/10.3390/math8081233Cite-Link
URN (Cite-Link):https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200811-42125Cite-Link
URL:https://www.mdpi.com/2227-7390/8/8/1233
Parent Title (English):Mathematics
Publisher:MDPI
Place of publication:Basel
Language:English
Date of Publication (online):2020/07/27
Year of first Publication:2020
Release Date:2020/08/11
Publishing Institution:Bauhaus-Universität Weimar
Institutes:Fakultät Bauingenieurwesen / Institut für Strukturmechanik
Volume:2020
Issue:volume 8, issue 8, article 1233
Pagenumber:15
Tag:dimensionality reduction; machine learning; principal component analysis; water quality; wavelet transform
GND Keyword:Maschinelles Lernen
Dewey Decimal Classification:500 Naturwissenschaften und Mathematik / 510 Mathematik
BKL-Classification:31 Mathematik
Open Access Publikationsfonds:Open-Access-Publikationsfonds 2020
Licence (German):License Logo Creative Commons 4.0 - Namensnennung (CC BY 4.0)