TY  - JOUR
A1  - Meng, Yinghui
A1  - Noman Qasem, Sultan
A1  - Shokri, Manouchehr
A1  - Shamshirband, Shahaboddin
T1  - Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis
T2  - Mathematics
N2  - 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.
KW  - Maschinelles Lernen
KW  - machine learning
KW  - dimensionality reduction
KW  - wavelet transform
KW  - water quality
KW  - principal component analysis
KW  - OA-Publikationsfonds2020
Y1  - 2020
UR  - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4212
UR  - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200811-42125
UR  - https://www.mdpi.com/2227-7390/8/8/1233
VL  - 2020
IS  - volume 8, issue 8, article 1233
PB  - MDPI
CY  - Basel
ER  -