An improved pore-solid fractal model for predicting coastal saline soil hydraulic properties based on changepoints determined by genetic algorithm-support vector regression

Coastal soil is an important reserve land resource. The accurate prediction of soil hydraulic properties plays an important role in understanding the improvement of soil properties after reclamation in coastal areas. With the development of modern mathematical theory, the pore-solid fractal (PSF) model has become an important basis for simulating soil hydraulic properties. The accuracy of PSF model depends on the accurate acquisition of changepoints and fractal dimensions. Previous studies have found that micro-CT scanning combined with image processing could accurately obtain fractal dimensions. However, we still need soil water retention data to determine changepoints, which greatly limits the application of fractal models for estimating soil hydraulic properties. In this case, we determined the relationship between changepoints and soil physical and chemical properties and established a genetic algorithm-support vector regression (GA-SVR) model to predict changepoints. Then an improved PSF model was adopted to predict soil water retention curve in coastal areas of Jiangsu Province based on image processing. The results showed that soil physical and chemical properties changed the soil water movement and changepoints by affecting soil pore distribution. In coastal saline soil, four parameters (BD, silt, EC, SOM) were selected to predict changepoints. The mass fractal dimension (Dm) was mainly influenced by the porosity and the heterogeneity of pore space. The porosity, pore diameter and specific surface area were the determining factors of DS value. Reclamation activities in coastal reclamation area changed Dm value but had no clear effect on DS value. By combining predicted changepoints, measured water content at the suction of changepoints and fractal dimensions, the accuracy of the PSF model had been greatly improved.