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Poster

Forest Technology

AI, Machine Learning, and Ground Truth Data Improve Wildfire Risk Management and Biomass Prediction Accuracy

Thursday, September 19, 2024
9:30 AM – 10:30 AM MST
Location: McKee Hall
This study evaluates remote sensing and GIS tools for natural resource management, highlighting their integration with in-situ validation. It assesses machine learning models for forest fire prediction in South Carolina, finding Decision Tree models most accurate. Additionally, it validates a Random Forest model for biomass estimation, emphasizing its effectiveness.

Learning Objectives:

  • Importance of AI.
  • Articles published using different tools in natural resource management.
  • Know smart technology in natural resource management.
  • The importance of ground truth data
  • They can identify the factors that affect the fire.
  • Factors that predict the above ground biomass.