An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data
Date
2021
Authors
Thomas, Robert
Khan, Usman T.
Valeo, Caterina
Talebzadeh, Fatima
Journal Title
Journal ISSN
Volume Title
Publisher
Environments
Abstract
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.
Description
Keywords
fuzzy modelling, marine sediment, Takagi-Sugeno, ordinary kriging (OK), inverse distance weighting (IDW), spatial predictions
Citation
Thomas, R., Khan, U. T., Valeo, C., & Talebzadeh, F. (2021). An Investigation of Takagi- Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data. Environments, 8(6), 1-12. https://doi.org/10.3390/environments8060050.