Research on soil moisture prediction based on mechanism analysis and ARIMA model

Xiwen Huang

doi:10.1117/12.2686150

28 July 2023 Research on soil moisture prediction based on mechanism analysis and ARIMA model

Xiwen Huang

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 1275607 (2023) https://doi.org/10.1117/12.2686150
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

In order to better understand and explore the soil moisture prediction method, this paper establishes a soil moisture prediction model based on soil moisture data and related meteorological data from 2012-2021 in Xilin Gol grassland of Inner Mongolia, while keeping the current grazing strategy unchanged, based on Occam's Razor principle, using soil moisture and existing data such as soil evaporation and seasonal changes. Firstly, the ARIMA (12,1,0) model was developed to predict the precipitation and soil evaporation in 2022 and 2023. Second, according to the transformation relationship between evaporated water and latent heat flux, a linear model of soil moisture and independent variables such as precipitation and evaporation was established, and the expressions of different soil moisture and each influencing factor were obtained by fitting, and the land moisture data at different depths were calculated as the prediction results, in order to provide technical reference for further exploration of soil moisture forecasting and better meteorological services for agriculture. The results are intended to provide a technical reference for further exploration of soil moisture forecasting and to lay the foundation for better meteorological services for agriculture.

Citation Download Citation

Xiwen Huang "Research on soil moisture prediction based on mechanism analysis and ARIMA model", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 1275607 (28 July 2023); https://doi.org/10.1117/12.2686150

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KEYWORDS

Soil moisture

Data modeling

Atmospheric modeling

Soil science

Meteorology

Mathematical modeling

Statistical modeling

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