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A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland

Author

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  • Rong Fu

    (College of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Luze Xie

    (College of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Tao Liu

    (Department of Sociology, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Binbin Zheng

    (College of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Yibo Zhang

    (College of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Shuai Hu

    (College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

Soil moisture plays an important role in ecology, hydrology, agriculture and climate change. This study proposes a soil moisture prediction model, based on the depth and water balance equation, which integrates the water balance equation with the seasonal ARIMA model, and introduces the depth parameter to consider the soil moisture at different depths. The experimental results showed that the model proposed in this study was able to provide a higher prediction accuracy for the soil moisture at 40 cm, 100 cm and 200 cm depths, compared to the seasonal ARIMA model. Different models were used for different depths. In this study, the seasonal ARIMA model was used at 10 cm, and the proposed model was used at 40 cm, 100 cm and 200 cm, from which more accurate prediction values could be obtained. The fluctuation of the predicted data has a certain seasonal trend, but the regularity decreases with the increasing depth until the soil moisture is almost independent of the external influence at a 200 cm depth. The accurate prediction of the soil moisture can contribute to the scientific management of the grasslands, thus promoting ecological stability and the sustainable development of the grasslands while rationalizing land use.

Suggested Citation

  • Rong Fu & Luze Xie & Tao Liu & Binbin Zheng & Yibo Zhang & Shuai Hu, 2023. "A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland," IJERPH, MDPI, vol. 20(2), pages 1-18, January.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:2:p:1374-:d:1033072
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Qian Zhu & Yulin Luo & Dongyang Zhou & Yue-Ping Xu & Guoqing Wang & Ye Tian, 2021. "Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(2), pages 2161-2185, January.
    3. Babak Mohammadi, 2022. "Application of Machine Learning and Remote Sensing in Hydrology," Sustainability, MDPI, vol. 14(13), pages 1-2, June.
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    Cited by:

    1. Zefu Gao & Qinyu Zhu & Haicheng Tao & Yiwen Jiao, 2023. "Grassland Health in Xilin Gol League from the Perspective of Machine Learning—Analysis of Grazing Intensity on Grassland Sustainability," Sustainability, MDPI, vol. 15(4), pages 1-31, February.

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