This research aims to use machine learning algorithm to predict wildfires impact on green landscape based on weather conditions while exploring factors that could influence the possibility. By utilizing the generated results, it could help decision-makers and authorities to come-up with best possible strategy to prepare for and respond to wildfires. Being prepare will work toward the economy best interest by saving unintended expenditure in time of crises also saves the cash liquidity along the economy on the long-term. For example, back in East cost of United States of America in Florida, in 1998, the estimated economical losses because of wildfires reached $880 million as it burned around 202,00 hectares. Yet on the west coast in San Diego, a county in California United State of America, the estimated economical losses are $2.450 billion.
Furthermore, According to World Bank, Indonesia estimated economical losses in 2015 from 2,611 million hectares burned because of wildfires reached $ 5.2 billion which equals 0.5% of the country domestic growth.
There are various factors that could start or increase the area of wildfire which includes but not- limited-to droughts, temperature, wind speed and vegetation. The proposal is aimed to explore different factors that influence wildfires and wildfires effect on vegetation by using open-source Australia dataset obtained from IBM as case study while using Python for data understanding, prepossessing, and visualization.
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research (Dubai)
Al-Khuraisat, Saif, "Using Decision-Tree Analysis for Predicting Wildfires Impact on Vegetation in Australia" (2023). Thesis. Rochester Institute of Technology. Accessed from