Econ. Environ. Geol. 2003; 36(3): 243-255

Published online June 30, 2003

© THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY

GIS-based Data-driven Geological Data Integration using Fuzzy Logic: Theory and Application

No-Wook Park1*, Kwang-Hoon Chi1, Chang-Jo F. Chung2 and Byung-Doo Kwon3

1Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources
2Geological Survey of Canada, 3Department of Earth Sciences, Seoul National University

Correspondence to :

No-Wook Park

nwpark@kigam.re.kr

Received: May 22, 2003; Accepted: June 21, 2003

Abstract

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy
inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and γ operator shows the better prediction power than the results using max and min operators from the validation procedure.

Keywords fuzzy logic, data-driven representation, spatial integration, geological information

Article

Econ. Environ. Geol. 2003; 36(3): 243-255

Published online June 30, 2003

Copyright © THE KOREAN SOCIETY OF ECONOMIC AND ENVIRONMENTAL GEOLOGY.

GIS-based Data-driven Geological Data Integration using Fuzzy Logic: Theory and Application

No-Wook Park1*, Kwang-Hoon Chi1, Chang-Jo F. Chung2 and Byung-Doo Kwon3

1Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources
2Geological Survey of Canada, 3Department of Earth Sciences, Seoul National University

Correspondence to:

No-Wook Park

nwpark@kigam.re.kr

Received: May 22, 2003; Accepted: June 21, 2003

Abstract

The mathematical models for GIS-based spatial data integration have been developed for geological applications such as mineral potential mapping or landslide susceptibility analysis. Among various models, the effectiveness of fuzzy logic based integration of multiple sets of geological data is investigated and discussed. Unlike a traditional target-driven fuzzy integration approach, we propose a data-driven approach that is derived from statistical relationships between the integration target and related spatial geological data. The proposed approach consists of four analytical steps; data representation, fuzzy combination, defuzzification and validation. For data representation, the fuzzy membership functions based on the likelihood ratio functions are proposed. To integrate them, the fuzzy
inference network is designed that can combine a variety of different fuzzy operators. Defuzzification is carried out to effectively visualize the relative possibility levels from the integrated results. Finally, a validation approach based on the spatial partitioning of integration targets is proposed to quantitatively compare various fuzzy integration maps and obtain a meaningful interpretation with respect to future events. The effectiveness and some suggestions of the schemes proposed here are illustrated by describing a case study for landslide susceptibility analysis. The case study demonstrates that the proposed schemes can effectively identify areas that are susceptible to landslides and γ operator shows the better prediction power than the results using max and min operators from the validation procedure.

Keywords fuzzy logic, data-driven representation, spatial integration, geological information

    KSEEG
    Jun 30, 2024 Vol.57 No.3, pp. 281~352

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