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

Poster Presenter(s)

Sanjeev Sharma, n/a

Graduate Research Assistant
Clemson University
Clemson, South Carolina, United States

Author(s)

Puskar Khanal, n/a (he/him/his)

Asst. Professor
Clemson University
Clemson, South Carolina, United States

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.