In this study, Logistics Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Multilayer Perceptron (MLP) machine learning algorithms are combined with GIS techniques to map landslide susceptibility in Shexian County, China. By using satellite images and various topographic and geological maps, 16 landslide susceptibility factor maps of Shexian County were initially constructed. In total, 502 landslide and random safety points were then using the “Extract Multivalues To Points” tool in ArcGIS, parameters for the 16 factors were extracted and imported into models for the five algorithms, of which 70% of samples were used for training and 30% of samples were used for verification, which makes sense for date symmetry. The Shexian grid was converted into 260130 vector points and imported into the five models, and the natural breakpoint method was used to divide the grid into four levels: low, moderate, high, and very high. Finally, by using column results gained using Area Under Curve (AUC) analysis and a grid chart, susceptibility results for mapping landslide prediction in Shexian County was compared using the five methods. Results indicate that the ratio of landslide points of high or very high levels from LR, SVM, RF, GBM, and MLP was 1.52, 1.77, 1.95, 1.83, and 1.64, and the ratio of very high landslide points to grade area was 1.92, 2.20, 2.98, 2.62, and 2.14, respectively. The success rate of training samples for the five methods was 0.781, 0.824, 0.853, 0.828, and 0.811, and prediction accuracy was 0.772, 0.803, 0.821, 0.815, and 0.803, respectively; the order of accuracy of the five algorithms was RF > SVM > MLP > GBM > LR. Our results indicate that the five machine learning algorithms have good effect on landslide susceptibility evaluation in Shexian area, with Random Forest having the best effect.

Groundwater model predictions are often inaccurate due to uncertainties in model structure, heterogeneous parameters, and initial conditions, leading to error accumulation during simulations. Traditional data assimilation (DA) faces severe computational challenges in high‐dimensional systems due to the costly inversion of large covariance matrices. In addition, the inaccurate estimation of background and observation error statistics introduces further biases. To address these challenges, we develop and evaluate an integrated framework that couples a computationally efficient deep learning surrogate model for rapid prediction with Latent Data Assimilation (LDA) to accurately correct simulations. The framework employs dimensionality reduction, specifically Proper Orthogonal Decomposition (POD), to project the high‐dimensional physical state into a low‐dimensional latent space. Data assimilation is then performed in this reduced space using the Ensemble Kalman Filter (EnKF). Results demonstrate that POD provides a robust representation of simulated concentration fields and interpolated observations for dimensionality reduction. The EnKF operating in the latent space effectively reduces prediction errors. Key advantages of the LDA framework include: enabling sparse observations to effectively inform global state updates through the low‐dimensional latent variables, achieving higher accuracy comparable to traditional physical‐space DA while requiring significantly fewer observations, and inherently filtering high‐frequency noise from the initial condition during the dimensionality reduction process. Collectively, these features establish LDA as a powerful and computationally efficient methodology for enhancing predictive accuracy and managing uncertainty in complex and high‐dimensional groundwater systems.

Geochemical reactions can alter groundwater flow by changing solution density and porous-medium properties. Representing these feedbacks requires consistent exchange of chemical states and physical properties between reaction, flow, and transport calculations. We present MF6PQC v1.0, a Python framework coupling groundwater flow and transport in MODFLOW 6 with geochemical reactions in PhreeqcRM. MF6PQC updates solution density and converts mineral-volume changes into changes in porosity, hydraulic conductivity, and diffusion coefficients. It supports three schemes for coupling transport and reactions while retaining existing flow and geochemical model-building workflows. Comparisons with two established reactive transport models showed agreement in component transport, mineral reactions, and the evolution of porous-medium properties. An idealized evaporating salt-lake model reproduced density-driven circulation and evaporite zonation. In a heterogeneous evaporite-leaching aquifer, dissolution concentrated flow along preferential pathways, and density feedback strengthened this redistribution. MF6PQC provides configurable coupling schemes and property relationships for examining these interactions in groundwater reactive transport.

《地球科学》(ISSN 1000-2383;CN42-1874/P) (原刊名《地球科学——中国地质大学学报》ISSN 1000-2383;CN42-1233/P)，创刊于1957年，是由教育部主管、中国地质大学主办的综合性地学学术理论期刊，月刊，2015年改名为《地球科学》,她以反映中国地质大学教学与科研最新的、高水平的基础地质、应用地质、资源与环境地质及地学工程技术研究成果为主要任务。