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Intelligent predicting of salt pond’s ion concentration based on support vector regression and neural network

  • Jun Liu
  • Aowen XiaoEmail author
  • Guangyuan Lei
  • Guangfeng Dong
  • Mengting Wu
Smart Data Aggregation Inspired Paradigm & Approaches in IoT Applns
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Abstract

The constant dynamic changes in salt pond make it difficult to achieve accurate prediction of ion concentration. It is of great significance to get the accurate prediction of potassium ion concentration in salt pools for the actual production of potash fertilizer. In this paper, some machine learning methods, such as support vector regression (SVR), AdaBoost regressor, K neighbor regressor, gradient boosting regressor, extra trees regressor and neural network regressor, have been used to build the prediction models. In the experiment, the MSE and R2 of the K+ concentration by using SVR in test data set reach 0.26385 and 0.9414, which are better than other models. Therefore, the SVR model has high research value in the field of salt pool ion concentration prediction.

Keywords

Ion concentration Support vector regression Neural network AdaBoost Gradient boosting Extra trees Potash fertilizer 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61172150, 61803286), the Foundation of Hubei Provincial Key Laboratory of Intelligent Robot (HBIR 201802) and the tenth Graduate Innovation Fund of Wuhan Institute of Technology (CX2018197, CX2018200, CX2018212).

Compliance with ethical standards

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Jun Liu
    • 1
    • 2
  • Aowen Xiao
    • 1
    • 2
    Email author
  • Guangyuan Lei
    • 3
  • Guangfeng Dong
    • 3
  • Mengting Wu
    • 1
    • 2
  1. 1.Hubei Key Laboratory of Intelligent RobotWuhan Institute of TechnologyWuhanChina
  2. 2.School of Computer Science and EngineeringWuhan Institute of TechnologyWuhanChina
  3. 3.SDIC Xinjiang Luobupo Potash Co., Ltd.HamiChina

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