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

This study combined process-driven hydrological mechanism models with data-driven deep learning models, considering various factors like hydrology, meteorology, environment, and the interconnected effects of upstream and downstream rivers, to develop an interpretable hybrid streamflow simulation model.

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

This study proposes a physics-informed hybrid modeling framework that integrates the GR4J conceptual model with the XGBoost machine learning algorithm, enabling a process-based representation of catchment dynamics within the machine learning framework.

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...