Few studies of frost heave mechanisms have considered multifactor interactions, particularly in unsaturated saline soils typical of northeastern China. We collected soil samples in western Jijin Province and assessed their potential frost heave behavior with reference to four controllable factors: soluble salt content (CSS), compactness (C), temperature (T), and water content (WC) using a two-level split-plot experiment. The resulting frost heave ratio was between −0.6% and 2.1%. Analysis of variance showed that water content, compactness, and temperature had significant effects on frost heave behavior, with water content having the strongest correlation (factor coefficient of 0.82), while content of soluble salt (CSS) had no significant effect. The interaction factors (products of single factors) CSS × WC and C × WC had significant effects on frost heave behavior. A correlation analysis using these interaction factors with experimental data drawn from previous research showed results consistent with the improved frost heave experiment as the significant effects of single factors on frost heave behavior ranked from WC > C > T and the interaction factors CSS × WC and C × WC gain had significant effects. We then established two generalized regression neural network (GRNN) models based on the single and interaction factors in order to predict frost heave behavior, showing that adding the latter to the input dataset improved the model accuracy. Thus, future research on predicting frost heave behavior in unsaturated saline soils should consider multiple interacting factor for greater accuracy.

As a major tributary of the Yellow River, the Yiluo River holds vital importance for regional water resource management and ecological sustainability. In this study, the SWAT (version 2012) and PLUS models were used in combination to simulate the hydrological responses of the basin and to analyze how land use changes have influenced runoff dynamics over time. During the calibration and validation periods, the Nash–Sutcliffe efficiency coefficient (NS) and coefficient of determination (R²) for the SWAT model both exceeded 0.8, while the Kappa coefficient for the PLUS model indicated an overall accuracy of 0.91, confirming the applicability of both models to the Yiluo River Basin. However, despite strong annual performance, potential monthly or seasonal simulation uncertainties should be acknowledged and warrant further analysis. From 2000 to 2020, the areas of forest land, water, urban land, and unused land in the Yiluo River Basin increased by 795.15 km², 29.33 km², 573.67 km², and 0.25 km², respectively, while cultivated land and grassland decreased by 814.50 km² and 583.89 km². The spatial distribution of the annual average runoff depth generally exhibited a pattern of “higher in the upstream and lower in the downstream”. An increase in the forestland and grassland areas was found to suppress runoff generation, whereas the expansion of urban land promoted runoff production. Implementing water-sensitive land use strategies—such as expanding forest cover and conserving grasslands—is crucial for reducing the negative hydrological impacts of urban land expansion. Such measures can improve runoff regulation, enhance groundwater recharge, and support the sustainable management of water resources within the basin. Assuming climate conditions remain constant, land use in the Yiluo River Basin in 2025 and 2030 is expected to remain dominated by cultivated land and forestland. Under this scenario, the annual average runoff is projected to increase by 0.42% and 0.51% compared to in 2020, respectively.