Abstract
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.
Highlights
Soil moisture controls material and energy exchange at the ground-space interface, is the material bearer of heat transfer and energy exchange between the soil and atmospheric environment in the hydrological cycle [1], and is an important factor in maintaining the growth state of plants [2,3]
A genetic algorithm (GA)-back propagation (BP) regression prediction model was developed using 100 days of data obtained through a meteorological monitoring system as the experimental object
The first 90 days of monitoring data were used as the training set and 10 days of data were used as the test set, and the soil moisture of the ecological berm was simulated and predicted by neural network
Summary
Soil moisture controls material and energy exchange at the ground-space interface, is the material bearer of heat transfer and energy exchange between the soil and atmospheric environment in the hydrological cycle [1], and is an important factor in maintaining the growth state of plants [2,3]. Soil moisture promotes plant growth and development by improving nutrient uptake by plants, and proper soil moisture is essential for plant growth and development [4,5]. Soil moisture changes are influenced by two main factors: climate change and vegetation growth status, where climate change plays a decisive role in soil moisture trends and vegetation growth status plays a regulatory role under climate change conditions [8]. Accurate prediction of soil moisture can provide effective information for drought and flood control [9] and precise irrigation decisions [10,11] in plant cultivation, as well as improve the predictability of plant flowering and abiotic stress risk assessment [12]. We use machine learning methods to obtain meteorological data and predict soil moisture by meteorological factors, which are important for ecological conservation of slopes
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