Physical and data hybrid-driven modeling for the evolution state prediction of reservoir colluvial landslides with step-like movement patterns: a case study in the Xiluodu Reservoir area, China

Physics-informed neural networks incorporating slope stability mechanics for real-time landslide early warning in Three Gorges Reservoir Area

Landslides pose significant threats to infrastructure and public safety in reservoir and mountainous regions, requiring accurate and physically reliable early-warning systems. This study proposes a Physics-Informed Neural Network integrated with the Marine Predators Algorithm (PINN + MPA) for real-time landslide deform...

Integrating Physical-Based Xinanjiang Model and Deep Learning for Interpretable Streamflow Simulation: A Multi-Source Data Fusion Approach across Diverse Chinese Basins

Abstract. The simulation of streamflow is a complex task due to its intricate formation process. Existing single models struggle to accurately capture the stochastic, non-stationary, and nonlinear dynamics of basin streamflow in changing environments. This study combined process-driven hydrological mechanism models wit...

Physics-informed hybrid GR4J–XGBoost model for streamflow prediction: integrating conceptual states, SHAP interpretability, and uncertainty analysis

Accurate streamflow prediction remains challenging due to the nonlinear and dynamic nature of rainfall–runoff processes. Conceptual hydrological models provide physically interpretable representations, yet their predictive performance is often limited, while data-driven models may suffer from overfitting and a lack of...

Predicting Thermal Structure in Large Reservoirs Under Climate Change Using Machine Learning

A CEEMDAN–SVR–PSO-LSTM Hybrid Model for Construction-Induced Displacement Prediction of Metro Deep Excavations

Accurate prediction of retaining-pile displacement is important for deformation control during staged deep-excavation construction, where monitoring series often exhibit pronounced nonstationarity and multiscale temporal variation. To account for the distinct temporal characteristics of the trend and fluctuation compon...

X-SEL: a stacked ensemble learning architecture for forecasting soil slide displacement in flume simulations

The X-SEL framework developed in this paper serves as a proof-of-principle of utilizing inexpensive IMU sensors for predicting soil sliding displacement using IMU sensor data from flume experiments and is aimed at expanding X-SEL beyond the flume environment, conducting uncertainty quantification, and designing practic...