Research on Prediction Method of Landslide Soil Moisture Content Based on Machine Learning Algorithm
Soil moisture content is one of the decisive factors affecting slope stability.It is difficult to accurately per-ceive the soil moisture information inside the landslide.A soil moisture content prediction model(DDNN)based on ma-chine learning algorithm dendritic neural network was established.After determining the key influencing factors by analy-zing the vertical variation characteristics of soil moisture and data correlation,the water prediction model was compared with GA-BP,RF and RBFNN algorithms.The results show that the goodness of fit R2 of the DDNN prediction model was 0.998,and the root mean square error and mean absolute error were the smallest,which were 0.091 and 0.059,re-spectively.The prediction accuracy was significantly higher than the other three algorithms.The relationship spectrum was used to explore the sensitivity of related influencing factors to soil moisture content.The results show that the sensi-tivity from high to low is temperature,precipitation,initial moisture,wind speed and ground temperature.The research results can provide technical support for the stability analysis of landslide.
万方数据
维普
