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JACIII Vol.20 No.6 pp. 992-1003
doi: 10.20965/jaciii.2016.p0992
(2016)

Paper:

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Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data

Dejian Wang*, Yoichi Kageyama**,†, Makoto Nishida**, and Hikaru Shirai**

*Dalian Nationalities University
18 Liaohexi Road, Kaifa Zone, Dalian 116600, China

**Graduate School of Engineering and Resource Science, Akita University
1-1 Tegata Gakuen-machi, Akita-shi, Akita 010-8502, Japan

†Corresponding author

Received:
April 27, 2016
Accepted:
August 30, 2016
Published:
November 20, 2016
Creative Commons License
Keywords:
remote sensing, ALOS, AVNIR-2, fuzzy multiple regression analysis, water quality
Abstract
The distribution of water pollution is often assessed by remote sensing. In this study, we develop a fuzzy multiple regression model and analyze water quality using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite at different time points. We conduct a fuzzy multiple regression analysis of the AVNIR-2 data and direct measurements of the local water quality of Lake Hachiroko in Japan. The relationship between the AVNIR-2 and water quality data are analyzed by solving both min and max problems. We compare the estimated water quality maps with the actual distributions in the study area, and determine that the proposed method enables us to derive water quality conditions effectively from the AVNIR-2 data. Furthermore, by comparing maps created using AVNIR-2 data collected at different times, we obtain results revealing temporal changes in water quality. In addition, we compare maps created using the fuzzy multiple regression and fuzzy regression models. We demonstrate that the former offers a greater number of solutions and provides more details about water quality.
Cite this article as:
D. Wang, Y. Kageyama, M. Nishida, and H. Shirai, “Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.6, pp. 992-1003, 2016.
Data files:
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