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Research on soil moisture prediction model based on deep learning

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  • Yu Cai
  • Wengang Zheng
  • Xin Zhang
  • Lili Zhangzhong
  • Xuzhang Xue

Abstract

Soil moisture is one of the main factors in agricultural production and hydrological cycles, and its precise prediction is important for the rational use and management of water resources. However, soil moisture involves complex structural characteristics and meteorological factors, and it is difficult to establish an ideal mathematical model for soil moisture prediction. Existing prediction models have problems such as prediction accuracy, generalization, and multi-feature processing capability, and prediction performance must improve. Based on this, taking the Beijing area as the research object, the deep learning regression network (DNNR) with big data fitting capability was proposed to construct a soil moisture prediction model. By integrating the dataset, analyzing the time series of the predictive variables, and clarifying the relationship between features and predictive variables through the Taylor diagram, selected meteorological parameters can provide effective weights for moisture prediction. Test results prove that the deep learning model is feasible and effective for soil moisture prediction. Its’ good data fitting and generalization capability can enrich the input characteristics while ensuring high accuracy in predicting the trends and values of soil moisture data and provides an effective theoretical basis for water-saving irrigation and drought control.

Suggested Citation

  • Yu Cai & Wengang Zheng & Xin Zhang & Lili Zhangzhong & Xuzhang Xue, 2019. "Research on soil moisture prediction model based on deep learning," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-19, April.
  • Handle: RePEc:plo:pone00:0214508
    DOI: 10.1371/journal.pone.0214508
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    References listed on IDEAS

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    1. Jackson, Scott H., 2003. "Comparison of calculated and measured volumetric water content at four field sites," Agricultural Water Management, Elsevier, vol. 58(3), pages 209-222, February.
    2. Hashemy Shahdany, S. Mehdy & Firoozfar, Alireza & Maestre, J.M. & Mallakpour, Iman & Taghvaeian, Saleh & Karimi, Poolad, 2018. "Operational performance improvements in irrigation canals to overcome groundwater overexploitation," Agricultural Water Management, Elsevier, vol. 204(C), pages 234-246.
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    Cited by:

    1. Wu, Tsung-Hsi & Chen, Pei-Yuan & Chen, Chien-Chih & Chung, Meng-Ju & Ye, Zheng-Kai & Li, Ming-Hsu, 2024. "Classification and Regression Tree (CART)-based estimation of soil water content based on meteorological inputs and explorations of hydrodynamics behind," Agricultural Water Management, Elsevier, vol. 299(C).
    2. Yu, Jingxin & Zhang, Xin & Xu, Linlin & Dong, Jing & Zhangzhong, Lili, 2021. "A hybrid CNN-GRU model for predicting soil moisture in maize root zone," Agricultural Water Management, Elsevier, vol. 245(C).
    3. Mahmoudi, Neda & Majidi, Arash & Jamei, Mehdi & Jalali, Mohammadnabi & Maroufpoor, Saman & Shiri, Jalal & Yaseen, Zaher Mundher, 2022. "Mutating fuzzy logic model with various rigorous meta-heuristic algorithms for soil moisture content estimation," Agricultural Water Management, Elsevier, vol. 261(C).

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