Research on soil moisture prediction based on VAR-ARIMA model
DOI:
https://doi.org/10.54097/hset.v42i.7121Keywords:
Soil moisture, ARIMA time series, VAR model.Abstract
This study uses precipitation, soil moisture and evapotranspiration data as independent variables to predict future soil moisture in the Xilingol grassland of Inner Mongolia, China, while keeping the grazing strategy unchanged. Firstly, the data with anomalous values in the obtained dataset were differenced, and the soil moisture data for different depths were split to build a multi-group ARIMA prediction model. The reliability of the model was determined to obtain the soil moisture in grassland at different depths from 2022 to 2023. Through this study, we have added VAR to the ARIMA model to improve the limitations of predicting future soil moisture based on a single variable and to improve the accuracy of soil moisture prediction, thus providing some theoretical support for the ecological restoration and sustainable development of grasslands.
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