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    Evaluating geo-environmental variables using a clustering based areal model
    (Pergamon-Elsevier Science Ltd, 2012) Tutmez, Bulent; Kaymak, Uzay; Tercan, A. Erhan; Lloyd, Christopher D.
    Global regression models do not accurately reflect the spatial heterogeneity which characterises most geo-environmental variables. In analysing the relationships between such variables, an approach is required which allows the model parameters to vary spatially. This paper proposes a new framework for exploring local relationships between geo-environmental variables. The method is based on extended objective function based fuzzy clustering with the environmental parameters estimated through on a locally weighted regression analysis. The case studies and prediction evaluations show that the fuzzy algorithm yields well-fitted models and accurate predictions. In addition to an increased accuracy of prediction relative to the widely-used geographically weighted regression (GWR), the proposed algorithm provides the search radius (bandwidth) and weights for local estimation directly from the data. The results suggest that the method could be employed effectively in tackling real world kernel-based modelling problems. (C) 2012 Elsevier Ltd. All rights reserved.
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    Fuzzy optimization of slab production from mechanical stone properties
    (Springer, 2008) Tutmez, Bulent; Kaymak, Uzay
    This paper aims to conduct slab production optimization by a flexible tool, which is fuzzy linear programming. There is a direct relationship between slab production and mechanical stone characteristics. In this process, the goal and its tolerance cannot be specified firstly due to a lack of knowledge. Therefore, the optimal system design problem for optimal slab production under soft constraints is constructed and solved in a fuzzy environment. The results show that fuzzy linear optimization is a convenient tool for optimizing slab production.
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    Local models for the analysis of spatially varying relationships in a lignite deposit
    (European Soc Fuzzy Logic & Technology, 2009) Tutmez, Bulent; Tercan, A. Erhan; Kaymak, Uzay; Lloyd, Christopher D.
    Relationships between geographically referenced variables are usually spatially heterogeneous and, to account for such variations, local models are necessary. This paper compares the Geographically Weighted Regression (GWR) model, usually used to integrate and examine the spatial heterogeneity of a relationship, and the Fuzzy Clustering-Based Least Squares (FCBLS) model for the analysis of spatially varying relationships. Both models use the same model parameters and bandwidth values derived from the Akaike Information Criterion. The results show that FCBLS outperforms the GWR model.
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    Local spatial regression models: a comparative analysis on soil contamination
    (Springer, 2012) Tutmez, Bulent; Kaymak, Uzay; Tercan, A. Erhan
    Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have been evaluated. The experimental studies showed that integration of objective function based fuzzy clustering to geostatistics provides some accurate and general models structures. In particular, the estimation performance of the model established by combining the extended fuzzy clustering algorithm and standard regional dependence function is higher than that of the other regression models. Finally, it could be suggested that the hybrid regression models developed by combining soft computing and geostatistics could be used in spatial data analysis.
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    Measure of uncertainty in regional grade variability
    (Springer-Verlag Berlin, 2007) Tutmez, Bulent; Kaymak, Uzay
    Because the geological events are neither homogeneous nor isotropic, the geological investigations are characterized by particularly high uncertainties. This paper presents a hybrid methodology for measuring of uncertainty in regional grade variability. In order to evaluate the fuzziness in grade values at ore deposit, point cumulative semimadogram (PCSM) measure and a metric distance have been employed. By using the experimental PCSMs and their linear models, measures of fuzziness have been carried out for each location. Finally, an uncertainty map, which defines the regional variation of the uncertainty in different categories, has been composed.
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    Regional Spatial Analysis Combining Fuzzy Clustering and Non-parametric Correlation
    (Springer-Verlag Berlin, 2013) Tutmez, Bulent; Kaymak, Uzay
    In this study, regional analysis based on a limited number of data, which is an important real problem in some disciplines such as geosciences and environmental science, was considered for evaluating spatial data. A combination of fuzzy clustering and non-parametrical statistical analysis is made. In this direction, the partitioning performance of a fuzzy clustering on different types of spatial systems was examined. In this way, a regional projection approach has been constructed. The results show that the combination produces reliable results and also presents possibilities for future works.