Modeling of Soil Moisture Data by ARMA Time Series

Abstract

Abstract The use of known data to predict future environmental parameters plays a crucial role in agriculture. In this paper, we propose a novel time series prediction method that combines the Auto-Regressive Moving Average Model (ARMA) and Gradient Boost Regression Tree(GBR) to forecast future soil moisture values. Firstly, the optimal number of decomposition modes for AMRA is determined by using Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF) plots. Secondly, according to statistics of XIlin Gol grassland offered by the Huawei Cup Mathematical Modeling Contest in 2022, the data including soil evaporation, precipitation, and soil moisture in the past ten years, are used as input parameters of ARMA to predict the precipitation and soil evaporation from 2022 to 2023. Then, the superiority of GBR was verified by comparing algorithms such as Support Vector Regression (SVR) and Random Forest(RF). Finally, GBR was used to realize the prediction for different soil moisture values from 2022 to 2023.