An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data
Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform labora...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-05-01
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| Series: | Environments |
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| Online Access: | https://www.mdpi.com/2076-3298/8/6/50 |
| _version_ | 1797532099889070080 |
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| author | Robert Thomas Usman T. Khan Caterina Valeo Mahta Talebzadeh |
| author_facet | Robert Thomas Usman T. Khan Caterina Valeo Mahta Talebzadeh |
| author_sort | Robert Thomas |
| collection | DOAJ |
| description | Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects. |
| first_indexed | 2024-03-10T10:54:17Z |
| format | Article |
| id | doaj.art-0ee25d227dd34c5ba4f8110f61825b41 |
| institution | Directory Open Access Journal |
| issn | 2076-3298 |
| language | English |
| last_indexed | 2024-03-10T10:54:17Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environments |
| spelling | doaj.art-0ee25d227dd34c5ba4f8110f61825b412023-11-21T21:58:22ZengMDPI AGEnvironments2076-32982021-05-01865010.3390/environments8060050An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial DataRobert Thomas0Usman T. Khan1Caterina Valeo2Mahta Talebzadeh3Department of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, CanadaDepartment of Civil Engineering, Lassonde School of Engineering, Toronto, ON M3J 1P3, CanadaDepartment of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, CanadaDepartment of Mechanical Engineering, University of Victoria, Victoria, BC V8W 2Y2, CanadaFuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects.https://www.mdpi.com/2076-3298/8/6/50fuzzy modellingmarine sedimentTakagi–Sugenoordinary kriging (OK)inverse distance weighting (IDW)spatial predictions |
| spellingShingle | Robert Thomas Usman T. Khan Caterina Valeo Mahta Talebzadeh An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data Environments fuzzy modelling marine sediment Takagi–Sugeno ordinary kriging (OK) inverse distance weighting (IDW) spatial predictions |
| title | An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data |
| title_full | An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data |
| title_fullStr | An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data |
| title_full_unstemmed | An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data |
| title_short | An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data |
| title_sort | investigation of takagi sugeno fuzzy modeling for spatial prediction with sparsely distributed geospatial data |
| topic | fuzzy modelling marine sediment Takagi–Sugeno ordinary kriging (OK) inverse distance weighting (IDW) spatial predictions |
| url | https://www.mdpi.com/2076-3298/8/6/50 |
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