Research on soil moisture content combination prediction model based on ARIMA and BP neural networks

Abstract

Predicting soil moisture accurately is the precondition of realizing accurate irrigation and improving the utilization rate of water resource and the necessary step of developing water‐saving agriculture, which can alleviate the water shortage in our agricultural effectively. In order to further improve the accuracy of soil water content prediction, a combined soil water content prediction model based on Autoregressive moving average model (ARIMA model) and back propagation neural network (BP neural network) neural network is proposed. The model considers the linear and nonlinear characteristics of soil water content data, combines them according to the characteristics of the model itself, gives full play to the advantages of ARIMA model and BP neural network. At the same time, two data smoothing methods were used to establish the ARIMA model, and the adaptive moment estimation algorithm (Adam algorithm) and mind evolutionary algorithm (MEA) optimization BP neural network model were used to propose an improved combined prediction model to predict soil water content data. The experimental results show that the average relative error of the improved combinatorial prediction model is 1.51%, which is 4.18%, 0.95% and 3.1% lower than the combinatorial prediction model, BP neural network model and ARIMA model, respectively, and the overall prediction effect is better, which can be used to save agricultural water and provide a strong basis for the development of water‐saving agriculture in China. At the same time, it can also ensure that crop production is increased and the purpose of national food security is guaranteed.

Enhancing Subsurface Soil Moisture Forecasting: A Long Short-Term Memory Network Model Using Weather Data

“…Statistical models, such as Autoregressive Integrated Moving Average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Vector Autoregression (VAR), and advanced RNNs like LSTM networks are also used for regional soil moisture forecasting depending on time series data. Wang et al (2023) studied ARIMA and Back Propagation neural network model and found that a combination of the two gives superior forecasting accuracy than individual models [21]. Singh et al (2020) used LSTM for regional soil moisture forecasting based on previous history for 5-25 cm soil depth [22].…”

Prediction of soil moisture using BiGRU-LSTM model with STL decomposition in Qinghai–Tibet Plateau

“…In the 1970s, the autoregressive integrated moving average (ARIMA) model became the central topic of time series analysis. Some studies have proposed combining the ARIMA model with the BP neural network model to simultaneously consider the linear and nonlinear characteristics of soil moisture data, resulting in improved predictive performance compared to using a single model ( Wang, Han & Chang, 2023a ). Furthermore, Wang et al (2023b) incorporated the GRU model into block Hankel tensor ARIMA, achieving even better results.…”