Prediction Interval Identification Using Interval Type-2 Fuzzy Logic Systems: Lake Water Level Prediction Using Remote Sensing Data

This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic prog...

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Bibliographic Details
Published in:IEEE sensors journal Vol. 21; no. 12; pp. 13815 - 13827
Main Authors: Khanesar, M. A., Branson, David T.
Format: Journal Article
Language:English
Published: New York IEEE 15.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Biological system modeling
Cost function
Data analysis
Estimation
Fuzzy logic
Fuzzy neural networks
Fuzzy systems
level measurement
Lower bounds
Parameter identification
Prediction algorithms
prediction interval
Predictive models
Quadratic programming
Remote sensing
Sensors
Support vector machines
Uncertainty
Upper bounds
Water levels
ISSN:1530-437X, 1558-1748
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Summary:This paper presents a novel approach to identify the prediction interval associated with data using interval type-2 fuzzy logic systems with support vector regression. For such a purpose, a constrained quadratic objective function is defined which is then solved using well-established quadratic programming approaches. Not only does the output of interval type-2 fuzzy logic system replicates the measured value, but also it provides the lower bound and the upper bound for measured data values. In the proposed approach, to have more valuable information, a penalty term is added in the cost functions to have full control over the width of prediction interval. This method has been successfully applied to two benchmark identification problems. It is observed that by using the control parameter in the cost function, it is possible to obtain a narrower, yet inclusive prediction interval. Furthermore, superior prediction accuracy is obtained compared to existing methods in literature. Motivated by these results, the proposed approach is used to predict time series collected using a satellite from Urmia lake water level which resulted in high accuracy and an inclusive prediction interval. The graphical abstract presented for the paper illustrates the overall data gathering as well as data analysis made to estimate the prediction interval associated with Urmia lake water level data.
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3067841