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Sensors (Basel). 2019 Oct 17;19(20). pii: E4517. doi: 10.3390/s19204517.

Classifying and Predicting Salinization Level in Arid Area Soil Using a Combination of Chua's Circuit and Fractional Order Sprott Chaotic System.

Author information

1
College of Information Engineering, Qujing Normal University, Qujing 655011, China. tianah@mail.qjnu.edu.cn.
2
College of Information Engineering, Qujing Normal University, Qujing 655011, China. fucb@mail.qjnu.edu.cn.
3
Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan. 40125451@gm.student.ncut.edu.tw.
4
Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan. htyau@ncut.edu.tw.
5
College of Applied Arts and Science, Beijing Union University, Beijing 100083, China. heigang@buu.edu.cn.
6
College of Resource and Environment Sciences, Xinjiang University, Urumqi 830046, China. heigang@buu.edu.cn.

Abstract

Soil salinization is very complex and its evolution is affected by numerous interacting factors produce strong non-linear characteristics. This is the first time fractional order chaos theory has been applied to soil salinization-level classification to decrease uncertainty in salinization assessment, solve fuzzy problems, and analyze the spectrum chaotic features in soil with different levels of salinization. In this study, typical saline soil spectrum data from different human interference areas in Fukang City (Xinjiang) and salt index test data from an indoor chemical analysis laboratory are used as the base information source. First, we explored the correlation between the spectrum reflectance features of soil with different levels of salinization and chaotic dynamic error and chaotic attractor. We discovered that the chaotic status error in the 0.6 order has the greatest change. The 0.6 order chaotic attractors are used to establish the extension matter-element model. The determination equation is built according to the correspondence between section domain and classic domain range to salinization level. Finally, the salt content from the chemical analysis is substituted into the discriminant equation in the extension matter-element model. Analysis found that the accuracy of the discriminant equation is higher. For areas with no human interference, the extension classification can successfully identify nine out of 10 prediction data, which is a 90% identification accuracy rate. For areas with human interference, the extension classification can successfully identify 10 out of 10 prediction data, which is a success rate of 100%. The innovation in this study is the building of a smart classification model that uses a fractional order chaotic system to inversely calculate soil salinization level. This model can accurately classify salinization level and its predictive results can be used to rapidly calculate the temporal and spatial distribution of salinization in arid area/desert soil.

KEYWORDS:

areas with different levels of human interference; arid area soil; dynamic error; extension matter-element model; fractional order compound master-slave chaotic system; salinization level

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