Effect of Decision Tree in the ANFIS Models: An Example of Completing Missing Data.
Saved in:
| Title: | Effect of Decision Tree in the ANFIS Models: An Example of Completing Missing Data. |
|---|---|
| Authors: | Saplioglu, K.1 (AUTHOR), Kucukerdem Ozturk, T. S.1 (AUTHOR) tulaykucukerdem@sdu.edu.tr |
| Source: | Russian Meteorology & Hydrology. May2024, Vol. 49 Issue 5, p435-445. 11p. |
| Subject Terms: | *Decision trees, *Missing data (Statistics), *Fuzzy logic, *Membership functions (Fuzzy logic), *Fuzzy systems, *Water supply |
| Geographic Terms: | Turkey |
| Abstract: | Missing data in water resources studies prevent planning. For this reason, data estimation studies are carried out. In this study, ANFIS (Adaptive Neural Fuzzy Inference System) was used to complete the missing data. At the study area, the Yesilirmak Basin located in the north of Turkey, input variables from seven stations and output variable from one station were determined. In the research, 80% (378 months of data) of 504 months of the flow data between 1969 and 2011 was used in the training phase and 20% (126 months of data) was employed in the testing one. The decision tree was used instead of the trial and error method in the selection of input variables and determining the number of membership functions in ANFIS models. It was concluded that the ANFIS model established with the information obtained from the decision tree is successful compared to the randomly established ANFIS models. Using the decision tree before ANFIS models are created will not only minimize the time spent on the model development, but also prevent the best of the possible models from being overlooked. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 178621641 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Effect of Decision Tree in the ANFIS Models: An Example of Completing Missing Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Saplioglu%2C+K%2E%22">Saplioglu, K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kucukerdem+Ozturk%2C+T%2E+S%2E%22">Kucukerdem Ozturk, T. S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> tulaykucukerdem@sdu.edu.tr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Russian+Meteorology+%26+Hydrology%22">Russian Meteorology & Hydrology</searchLink>. May2024, Vol. 49 Issue 5, p435-445. 11p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink><br />*<searchLink fieldCode="DE" term="%22Missing+data+%28Statistics%29%22">Missing data (Statistics)</searchLink><br />*<searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br />*<searchLink fieldCode="DE" term="%22Membership+functions+%28Fuzzy+logic%29%22">Membership functions (Fuzzy logic)</searchLink><br />*<searchLink fieldCode="DE" term="%22Fuzzy+systems%22">Fuzzy systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Water+supply%22">Water supply</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Turkey%22">Turkey</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Missing data in water resources studies prevent planning. For this reason, data estimation studies are carried out. In this study, ANFIS (Adaptive Neural Fuzzy Inference System) was used to complete the missing data. At the study area, the Yesilirmak Basin located in the north of Turkey, input variables from seven stations and output variable from one station were determined. In the research, 80% (378 months of data) of 504 months of the flow data between 1969 and 2011 was used in the training phase and 20% (126 months of data) was employed in the testing one. The decision tree was used instead of the trial and error method in the selection of input variables and determining the number of membership functions in ANFIS models. It was concluded that the ANFIS model established with the information obtained from the decision tree is successful compared to the randomly established ANFIS models. Using the decision tree before ANFIS models are created will not only minimize the time spent on the model development, but also prevent the best of the possible models from being overlooked. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=178621641 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3103/S1068373924050078 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 435 Subjects: – SubjectFull: Decision trees Type: general – SubjectFull: Missing data (Statistics) Type: general – SubjectFull: Fuzzy logic Type: general – SubjectFull: Membership functions (Fuzzy logic) Type: general – SubjectFull: Fuzzy systems Type: general – SubjectFull: Water supply Type: general – SubjectFull: Turkey Type: general Titles: – TitleFull: Effect of Decision Tree in the ANFIS Models: An Example of Completing Missing Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Saplioglu, K. – PersonEntity: Name: NameFull: Kucukerdem Ozturk, T. S. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 10683739 Numbering: – Type: volume Value: 49 – Type: issue Value: 5 Titles: – TitleFull: Russian Meteorology & Hydrology Type: main |
| ResultId | 1 |
