Research on Short-term Soil Moisture Prediction Model Based on Neural Networks
To achieve short-term soil moisture predication,a study was conducted on the short-term soil moisture forecasting model based on three years of data from 10 automatic meteorological soil moisture monitoring stations in Jizhou District,Jinghai District,Ninghe District,and Binhai New Area of Tianjin City.A total of 28 influencing factors,including station number,air temperature,air humidity,wind speed,and wind direction,were selected.Two sets of data,one including weather forecasts and one without weather forecasts,were used to train BP neural networks and Elman neural networks respectively.The results of the four prediction models were compared and analyzed.The results showed that the accuracy of the BP neural network model without weather forecasts and the BP neural network model with weather forecasts were 94.79%and 95.54%respectively,while the accuracy of the Elman neural network model without weather forecasts and the Elman neural network model with weather forecasts were 96.85%and 96.64%respectively.The study concluded that the Elman neural network has the characteristics of good stability and high accuracy.The theory suggests that the model accuracy with weather forecasts should be higher than that without weather forecasts,and the BP neural network shows this correlation,while the Elman neural network does not show this correlation.
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