Ensemble data assimilation methods for estimating fault slip and future earthquake occurrences

How predictable are macroscopic traffic states: a perspective of uncertainty quantification

Estimating geodynamic model parameters from geodetic observations using a particle method

Forecasting electricity demand of municipalities through artificial neural networks and metered supply point classification

Improved drought forecasting in Kazakhstan using machine and deep learning: a non-contiguous drought analysis approach

Forecasting House Prices through Credit Conditions: A Bayesian Approach

Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

A market for trading forecasts: A wagering mechanism

Ensemble Kalman, Adaptive Gaussian Mixture, and Particle Flow Filters for Optimized Earthquake Forecasting

Reporting delays: A widely neglected impact factor in COVID-19 forecasts

Improving the efficiency of renewable energy assets by optimizing the matching of supply and demand using a smart battery scheduling algorithm

From speculation to reality: Enhancing anticipatory ethics for emerging technologies (ATE) in practice

Integrated Forecasting and Scheduling of Implicit Demand Response in Balancing Markets Using Inverse Optimization

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Predictive heuristic control: Inferring risks from heterogeneous nowcast accuracy

Evaluation of the 3DVAR Operational Implementation of the Colombian Air Force for Aircraft Operations: A Case Study

Estimating the occurrence of slow slip events and earthquakes with an ensemble Kalman filter

Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling

Individual yield nowcasting for residential PV systems

Near-real-time satellite precipitation data ingestion into peak runoff forecasting models