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An Optimized Fuzzy System for Coastal Water Quality Mapping Using Remote Sensing Data

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Book cover Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10407))

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Abstract

In this paper, we propose coastal water quality mapping process using the combination of in-situ measurements and remote sensing data. Water maps is processed by an hybridization of fuzzy model and genetic algorithm which exploits remotely sensed multispectral reflectances to estimate coastal water quality. The relation between the water parameters and the subsurface reflectances is modeled by a set of fuzzy rules extracted automatically from the data through two steps procedure. First, fuzzy rules are generated by unsupervised fuzzy clustering of the input data. In the second step, genetic algorithm is applied to optimize the rules. Our contribution has focused on the use of several water parameter maps to construct a graphical tool named Pollution Signature Draw (PSD) in order to characterize the water quality. Water characterization is then evaluated by analyzing several types of PSDs related to typical sites selecting the most representatives’ ones. After, the selected PSDs are introduced in a classifier system to generate a pollution map (PM) associated the studied area. The proposed approach was tested on Algiers bay and has highlighted four pollution levels corresponding to High Pollution (HP), Medium Pollution (MP), Low Pollution (LP) and Clear Water (CW).

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References

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Authors and Affiliations

  1. Laboratory of Image Processing and Radiation, Faculty of Electronics and Computer Science, USTHB University, BP 32, El-Alia–Beb-Ezzouar, 16111, Algiers, Algeria

    Bahia Lounis & Aichouche Belhadj-Aissa

Authors
  1. Bahia Lounis
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  2. Aichouche Belhadj-Aissa
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Corresponding author

Correspondence to Bahia Lounis .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Lounis, B., Belhadj-Aissa, A. (2017). An Optimized Fuzzy System for Coastal Water Quality Mapping Using Remote Sensing Data. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10407. Springer, Cham. https://doi.org/10.1007/978-3-319-62401-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-62401-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62400-6

  • Online ISBN: 978-3-319-62401-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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