A GA-BP Neural Network Regression Model for Predicting Soil Moisture in Slope Ecological Protection

In this study, based on a highway project in Zhejiang, China, the meteorological factors and soil moisture of high side slopes were monitored in real time by a meteorological data monitoring system, and the correlation between soil moisture and meteorological factors was investigated using the obtained data of soil moisture and total solar radiation, atmospheric temperature, soil temperature, relative humidity, and wind speed. Based on the correlation and the influence of meteorological factors on soil moisture lag, a back propagation (BP) neural network regression model optimized with genetic algorithm (GA) was proposed for the first time and applied to soil moisture prediction of high side slopes. The results showed that the BP neural network regression model and the GA-BP neural network regression model were used for soil moisture prediction in two cases without and with lags, respectively, and both prediction methods showed a more significant improvement in prediction accuracy considering their lags compared with those without lags; the GA-BP neural network regression model outperformed the BP neural network regression model in terms of accuracy. V-fold cross-validation eliminated the effect of random errors, indicating that the model can be applied to soil moisture prediction for ecological conservation. Using the soil moisture prediction results as the basis for screening ecological slope protection vegetation is of great significance to the safety and reliability of road construction.

Automated summary

This study focused on a highway project in Zhejiang, China, where a meteorological data monitoring system was used to monitor the meteorological factors and soil moisture of high side slopes in real time. The correlation between soil moisture and meteorological factors was investigated, and a back propagation (BP) neural network regression model optimized with genetic algorithm (GA) was proposed for soil moisture prediction. The results showed that the model improved the prediction accuracy of soil moisture and had significant implications for ecological conservation.