FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3043640444> ?p ?o ?g. }

• W3043640444 endingPage "1233" @default.
• W3043640444 startingPage "1233" @default.
• W3043640444 abstract "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." @default.
• W3043640444 created "2020-07-23" @default.
• W3043640444 creator A5012596219 @default.
• W3043640444 creator A5013006277 @default.
• W3043640444 creator A5052418107 @default.
• W3043640444 creator A5080581779 @default.
• W3043640444 date "2020-07-27" @default.
• W3043640444 modified "2023-10-06" @default.
• W3043640444 title "Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis" @default.
• W3043640444 cites W1989767924 @default.
• W3043640444 cites W2017554280 @default.
• W3043640444 cites W2023567628 @default.
• W3043640444 cites W2036739084 @default.
• W3043640444 cites W2041534329 @default.
• W3043640444 cites W2046785557 @default.
• W3043640444 cites W2055179135 @default.
• W3043640444 cites W2057850952 @default.
• W3043640444 cites W2066996619 @default.
• W3043640444 cites W2079325629 @default.
• W3043640444 cites W2096777530 @default.
• W3043640444 cites W2117438123 @default.
• W3043640444 cites W2133097426 @default.
• W3043640444 cites W2172147742 @default.
• W3043640444 cites W2460819813 @default.
• W3043640444 cites W2594837422 @default.
• W3043640444 cites W2618548677 @default.
• W3043640444 cites W2743017312 @default.
• W3043640444 cites W2800887117 @default.
• W3043640444 cites W2806700095 @default.
• W3043640444 cites W2896575739 @default.
• W3043640444 cites W2940621945 @default.
• W3043640444 cites W2966800878 @default.
• W3043640444 cites W2977278354 @default.
• W3043640444 cites W4211007335 @default.
• W3043640444 doi "https://doi.org/10.3390/math8081233" @default.
• W3043640444 hasPublicationYear "2020" @default.
• W3043640444 type Work @default.
• W3043640444 sameAs 3043640444 @default.
• W3043640444 citedByCount "11" @default.
• W3043640444 countsByYear W30436404442021 @default.
• W3043640444 countsByYear W30436404442022 @default.
• W3043640444 countsByYear W30436404442023 @default.
• W3043640444 crossrefType "journal-article" @default.
• W3043640444 hasAuthorship W3043640444A5012596219 @default.
• W3043640444 hasAuthorship W3043640444A5013006277 @default.
• W3043640444 hasAuthorship W3043640444A5052418107 @default.
• W3043640444 hasAuthorship W3043640444A5080581779 @default.
• W3043640444 hasBestOaLocation W30436404441 @default.
• W3043640444 hasConcept C119857082 @default.
• W3043640444 hasConcept C124101348 @default.
• W3043640444 hasConcept C153180895 @default.
• W3043640444 hasConcept C154945302 @default.
• W3043640444 hasConcept C186108316 @default.
• W3043640444 hasConcept C195975749 @default.
• W3043640444 hasConcept C196216189 @default.
• W3043640444 hasConcept C202444582 @default.
• W3043640444 hasConcept C27438332 @default.
• W3043640444 hasConcept C33676613 @default.
• W3043640444 hasConcept C33923547 @default.
• W3043640444 hasConcept C41008148 @default.
• W3043640444 hasConcept C46286280 @default.
• W3043640444 hasConcept C47432892 @default.
• W3043640444 hasConcept C50644808 @default.
• W3043640444 hasConcept C58166 @default.
• W3043640444 hasConcept C70518039 @default.
• W3043640444 hasConceptScore W3043640444C119857082 @default.
• W3043640444 hasConceptScore W3043640444C124101348 @default.
• W3043640444 hasConceptScore W3043640444C153180895 @default.
• W3043640444 hasConceptScore W3043640444C154945302 @default.
• W3043640444 hasConceptScore W3043640444C186108316 @default.
• W3043640444 hasConceptScore W3043640444C195975749 @default.
• W3043640444 hasConceptScore W3043640444C196216189 @default.
• W3043640444 hasConceptScore W3043640444C202444582 @default.
• W3043640444 hasConceptScore W3043640444C27438332 @default.
• W3043640444 hasConceptScore W3043640444C33676613 @default.
• W3043640444 hasConceptScore W3043640444C33923547 @default.
• W3043640444 hasConceptScore W3043640444C41008148 @default.
• W3043640444 hasConceptScore W3043640444C46286280 @default.
• W3043640444 hasConceptScore W3043640444C47432892 @default.
• W3043640444 hasConceptScore W3043640444C50644808 @default.
• W3043640444 hasConceptScore W3043640444C58166 @default.
• W3043640444 hasConceptScore W3043640444C70518039 @default.
• W3043640444 hasIssue "8" @default.
• W3043640444 hasLocation W30436404441 @default.
• W3043640444 hasLocation W30436404442 @default.
• W3043640444 hasOpenAccess W3043640444 @default.
• W3043640444 hasPrimaryLocation W30436404441 @default.
• W3043640444 hasRelatedWork W2049364949 @default.
• W3043640444 hasRelatedWork W2090269531 @default.
• W3043640444 hasRelatedWork W2352079147 @default.
• W3043640444 hasRelatedWork W2355203151 @default.
• W3043640444 hasRelatedWork W2370292837 @default.
• W3043640444 hasRelatedWork W2371096191 @default.
• W3043640444 hasRelatedWork W4220663171 @default.
• W3043640444 hasRelatedWork W4280495727 @default.
• W3043640444 hasRelatedWork W94476185 @default.
• W3043640444 hasRelatedWork W2187285467 @default.
• W3043640444 hasVolume "8" @default.

• next