43 citations · 122 across the 16 of their papers we have counts for
8 papers · 1 filter
An operational framework to automatically evaluate the quality of weather observations from third-party stations
Quanxi Shao, Ming Li, Joel Janek Dabrowski +4
With increasing number of crowdsourced private automatic weather stations (called TPAWS) established to fill the gap of official network and obtain local weather information for va…
Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires
Joel Janek Dabrowski, Daniel Edward Pagendam, James Hilton +5
We apply the Physics Informed Neural Network (PINN) to the problem of wildfire fire-front modelling. We use the PINN to solve the level-set equation, which is a partial differentia…
Quality Control in Weather Monitoring with Dynamic Linear Models
Joel Janek Dabrowski, Ashfaqur Rahman, Ming Li +4
Decisions in agriculture are frequently based on weather. With an increase in the availability and affordability of off-the-shelf weather stations, farmers able to acquire localise…
Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution
Joel Janek Dabrowski, Daniel Edward Pagendam
We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks. The approach is more scalable to large data than Markov Ch…
Towards Data Assimilation in Level-Set Wildfire Models Using Bayesian Filtering
Joel Janek Dabrowski, Carolyn Huston, James Hilton +2
The level-set method is a prominent approach to modelling the evolution of a fire over time based on a characterised rate of spread. It however does not provide a direct means for…
A Spatio-Temporal Neural Network Forecasting Approach for Emulation of Firefront Models
Andrew Bolt, Carolyn Huston, Petra Kuhnert +3
Computational simulations of wildfire spread typically employ empirical rate-of-spread calculations under various conditions (such as terrain, fuel type, weather). Small perturbati…