Landslide susceptibility mapping (LSM) is significant for landslide risk assessment. However, there remains no consensus on which method is optimal for LSM. This study implements a dynamic approach to landslide hazard mapping by integrating spatio-temporal probability analysis with time-varying ground deformation velocity derived from the MT-InSAR (Multi-Temporal InSAR) method. Reliable landslide susceptibility maps (LSMs) can inform landslide risk managers and government officials. First, sixteen factors were selected to construct a causal factor system for LSM. Next, Pearson correlation analysis, multicollinearity analysis, information gain ratio, and GeoDetector methods were applied to remove the least important factors of STI, plan curvature, TRI, and slope length. Subsequently, information quantity (IQ), logistic regression (LR), frequency ratio (FR), artificial neural network (ANN), random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) methods were performed to construct the LSM. The results showed that the distance to a river, slope angle, distance from structure, and engineering geological rock group were the main factors controlling landslide development. A comprehensive set of statistical indicators was employed to evaluate these methods’ effectiveness; sensitivity, F1-measure, and AUC (area under the curve) were calculated and subsequently compared to assess the performance of the methods. Machine learning methods’ training and prediction accuracy were higher than those of statistical methods. The AUC values of the IQ, FR, LR, BP-ANN, RBF-ANN, RF, SVM, and CNN methods were 0.810, 0.854, 0.828, 0.895, 0.916, 0.932, 0.948, and 0.957, respectively. Although the performance order varied for other statistical indicators, overall, the CNN method was the best, while the BP-ANN and RBF-ANN method was the worst among the five examined machine methods. Hence, adopting the CNN approach in this study can enhance LSM accuracy, catering to the needs of planners and government agencies responsible for managing landslide-prone areas and preventing landslide-induced disasters.

This study experimentally investigated heavy metal removal and accumulation in the aquatic plant Eichhornia crassipes. Pb, Zn, Cd, and Mn concentrations, plant morphology, and plant functional groups were analyzed. Eichhornia crassipes achieved high removal efficiency of Pb and Mn from karst water (over 79.5%), with high proportion of Pb, Zn, and Cd absorption occurring in the first eight days. The highest removal efficiencies were obtained at initial Pb, Zn, Cd, and Mn concentrations of 1 mg/L, 2 mg/L, 0.02 mg/L, and 0.2 mg/L, respectively. Eichhornia crassipes exhibited a high bioconcentration factor (Mn = 199,567 > Pb = 19,605 > Cd = 3403 > Zn = 1913) and a low translocation factor (<1). The roots accumulated more Pb, Zn, Cd, and Mn than the stolons and leaves due to the stronger tolerance of roots. The voids, stomas, air chambers, and airways promoted this accumulation. Pb, Cd, Zn, and Mn likely exchanged with Mg, Na, and K through the cation exchange. C≡C, C=O, SO₄²⁻, O-H, C-H, and C-O played different roles during uptake, which led to different removal and accumulation effects.