1 citations · 1 across the 3 of their papers we have counts for
4 papers
QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Arpit Kapoor, Rohitash Chandra
Conceptual rainfall-runoff models aid hydrologists and climate scientists in modelling streamflow to inform water management practices. Recent advances in deep learning have unrave…
Ensemble quantile-based deep learning framework for streamflow and flood prediction in Australian catchments
Rohitash Chandra, Arpit Kapoor, Siddharth Khedkar +2
In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting…
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
Rohitash Chandra, Danial Azam, Arpit Kapoor +1
The complex and computationally expensive nature of landscape evolution models pose significant challenges in the inference and optimisation of unknown parameters. Bayesian inferen…
Surrogate-assisted parallel tempering for Bayesian neural learning
Rohitash Chandra, Konark Jain, Arpit Kapoor +1
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) metho…