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FIFUS: a rule-based fuzzy inference model for fuzzy spatial objects in spatial databases and GIS

Published: 03 November 2015 Publication History

Abstract

Decision support based on spatial (and not only alphanumerical) data has received increasing interest in geographical applications, such as geoscience, agriculture, and economics applications, and has led to Spatial Decision Support Systems (SDSS). SDSS use spatial database systems and Geographical Information Systems as their data management and analysis components in order to get and handle the needed spatial data and perform recommendations, estimations, or predictions. For instance, farmers want to know what the best areas of their farmland are to grow a specific crop. In most cases, the extent and the properties of the spatial phenomena of interest are vague and imprecise. They can be adequately represented by fuzzy spatial objects (e.g., fuzzy points, fuzzy lines, fuzzy regions). In this paper, we formally propose a model named Fuzzy Inference on Fuzzy Spatial Objects (FIFUS), which infers recommendations, estimations, and predictions based on fuzzy rules and knowledge of domain specialists. It incorporates fuzzy spatial objects into the components of the existing fuzzy inference methods in order to take into account the spatial imprecision found in the real world. As a main advantage, FIFUS is a general-purpose model and can thus be applied in many geoscience applications.

References

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A. Jadidi, M. A. Mostafavi, Y. Bédard, and K. Shahriari. Spatial representation of coastal risk: A fuzzy approach to deal with uncertainty. ISPRS International Journal of Geo-Information, 3(3):1077--1100, 2014.
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J.-S. Jang. Fuzzy inference systems. In Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, pages 73--91. Prentice-Hall, 1997.
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M. Schneider. Fuzzy spatial data types for spatial uncertainty management in databases. In J. Galindo, editor, Handbook of Research on Fuzzy Information Processing in Databases, pages 490--515. IGI Global, 2008.
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Cited By

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  • (2021)A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous Phenomena with Vague Boundaries Using Sensor DataSensors10.3390/s2120684021:20(6840)Online publication date: 14-Oct-2021
  • (2020)Situational Assessment of Wildfires: a Fuzzy Spatial Approach2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00179(1180-1185)Online publication date: Nov-2020
  • (2017)Fuzzy inference on fuzzy spatial objects (FIFUS) for spatial decision support systems2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2017.8015707(1-6)Online publication date: Jul-2017

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  1. FIFUS: a rule-based fuzzy inference model for fuzzy spatial objects in spatial databases and GIS

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    cover image ACM Conferences
    SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2015
    646 pages
    ISBN:9781450339674
    DOI:10.1145/2820783
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 November 2015

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    Author Tags

    1. fuzzy inference
    2. fuzzy spatial objects
    3. spatial decision support system
    4. spatial fuzziness

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    SIGSPATIAL '15 Paper Acceptance Rate 38 of 212 submissions, 18%;
    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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    View all
    • (2021)A Decentralized Fuzzy Rule-Based Approach for Computing Topological Relations between Spatial Dynamic Continuous Phenomena with Vague Boundaries Using Sensor DataSensors10.3390/s2120684021:20(6840)Online publication date: 14-Oct-2021
    • (2020)Situational Assessment of Wildfires: a Fuzzy Spatial Approach2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI50040.2020.00179(1180-1185)Online publication date: Nov-2020
    • (2017)Fuzzy inference on fuzzy spatial objects (FIFUS) for spatial decision support systems2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)10.1109/FUZZ-IEEE.2017.8015707(1-6)Online publication date: Jul-2017

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