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Parameter and State Estimation of One-Dimensional Infiltration Processes: A Simultaneous Approach

Author

Listed:
  • Song Bo

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Soumya R. Sahoo

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Xunyuan Yin

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Jinfeng Liu

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Sirish L. Shah

    (Department of Chemical & Materials Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

The Richards equation plays an important role in the study of agro-hydrological systems. It models the water movement in soil in the vadose zone, which is driven by capillary and gravitational forces. Its states (capillary potential) and parameters (hydraulic conductivity, saturated and residual soil moistures and van Genuchten-Mualem parameters) are essential for the accuracy of mathematical modeling, yet difficult to obtain experimentally. In this work, an estimation approach is developed to estimate the parameters and states of Richards equation simultaneously. In the proposed approach, parameter identifiability and sensitivity analysis are used to determine the most important parameters for estimation purpose. Three common estimation schemes (extended Kalman filter, ensemble Kalman filter and moving horizon estimation) are investigated. The estimation performance is compared and analyzed based on extensive simulations.

Suggested Citation

  • Song Bo & Soumya R. Sahoo & Xunyuan Yin & Jinfeng Liu & Sirish L. Shah, 2020. "Parameter and State Estimation of One-Dimensional Infiltration Processes: A Simultaneous Approach," Mathematics, MDPI, vol. 8(1), pages 1-22, January.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:1:p:134-:d:309521
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    References listed on IDEAS

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    1. Ritter, A. & Hupet, F. & Munoz-Carpena, R. & Lambot, S. & Vanclooster, M., 2003. "Using inverse methods for estimating soil hydraulic properties from field data as an alternative to direct methods," Agricultural Water Management, Elsevier, vol. 59(2), pages 77-96, March.
    2. Alejandro F Villaverde & Antonio Barreiro & Antonis Papachristodoulou, 2016. "Structural Identifiability of Dynamic Systems Biology Models," PLOS Computational Biology, Public Library of Science, vol. 12(10), pages 1-22, October.
    3. Ignacio Gomez Lucas & Jose Navarro-Pedreno & Maria Belen Almendro-Candel & Antonis Zorpas, 2018. "Physical Properties of Soils Affected by the Use of Agricultural Waste," Chapters, in: Anna Aladjadjiyan (ed.), Agricultural Waste and Residues, IntechOpen.
    4. Zhengzhong Yuan & Chen Zhao & Zengru Di & Wen-Xu Wang & Ying-Cheng Lai, 2013. "Exact controllability of complex networks," Nature Communications, Nature, vol. 4(1), pages 1-9, December.
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    Cited by:

    1. Sandra A. Obiri & Bernard T. Agyeman & Sarupa Debnath & Siyu Liu & Jinfeng Liu, 2023. "Sensor Selection and State Estimation of Continuous mAb Production Processes," Mathematics, MDPI, vol. 11(18), pages 1-20, September.

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