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Thomas Keeble

University of Melbourne

Carlton

Parkville, 3010

Australia

  • University of Melbourne

SCHOLARLY PAPERS

4

DOWNLOADS

215

TOTAL CITATIONS

1

Scholarly Papers (4)

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1.

Untethered from earthly constraints: A spatial seven-day ahead machine-learning forest fuel moisture forecasting system, independent of real-time sensor networks

Number of pages: 48 Posted: 20 Sep 2025
Thomas Keeble, Christopher S. Lyell, Tim Gazzard, Thomas Duff and Gary Sheridan
University of Melbourne, University of Melbourne, affiliation not provided to SSRN, affiliation not provided to SSRN and University of Melbourne
Downloads 68 (891,180)

Abstract:

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Fuel moisture, Wildfire, Machine learning, Forecasting, Spatial modelling, Risk management

2.

A Landscape Scale Post-Fire Debris Flow Frequency-Magnitude Impact Model

Number of pages: 45 Posted: 18 Jul 2023
Thomas Keeble, Christopher S. Lyell, Patrick N.J. Lane, Petter Nyman and Gary Sheridan
University of Melbourne, University of Melbourne, University of Melbourne, University of Melbourne and University of Melbourne
Downloads 57 (989,921)

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Geomorphic hazard, Initiation probability, Vulnerability assessment, Erosion

3.

A Model of Wind Speed Reduction in Forests that Can Be Parameterised Globally Using Spaceborne Lidar

Number of pages: 46 Posted: 13 Dec 2024
Molly Harrison, Philip J. Noske, Christopher S. Lyell, Kangmin Moon, Darcy P. Prior, Gary Sheridan and Thomas Keeble
affiliation not provided to SSRN, University of Melbourne, University of Melbourne, affiliation not provided to SSRN, affiliation not provided to SSRN, University of Melbourne and University of Melbourne
Downloads 46 (1,109,210)

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Fire behaviour, Wind reduction, Spaceborne-LIDAR, GEDI, wind speed

4.

Forecasting Dead Fuel Moisture Content Below Forest Canopies – a Seven-Day Forecasting System

Number of pages: 41 Posted: 15 May 2024
Christopher S. Lyell, Usha Nattala, Thomas Keeble, Elena Marie Vella, Rakesh Chandra Joshi, Zaher Joukhadar, Jonathan Garber, Simon Mutch, Tim Gazzard, Thomas Duff and Gary Sheridan
University of Melbourne, University of Melbourne - Melbourne Institute of Applied Economic and Social Research, University of Melbourne, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, affiliation not provided to SSRN, University of Melbourne, affiliation not provided to SSRN, affiliation not provided to SSRN and University of Melbourne
Downloads 44 (1,145,425)
Citation 1

Abstract:

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Canopy climate transfer, Machine learning, Forecasting, Dead fuel moisture content, Bushfire risk