Machine Learning Approaches for Estimating Human-Caused Wildfire Ignition Probability
Developing advanced models to predict human-caused wildfire ignitions using machine learning and geospatial data
Requirements
- M.Sc. in Machine Learning, Data Science, Computer Science, Mathematics, Telecommunications, or similar
- Knowledge of Python
- Software development skills
- Basic concepts of signal processing
- Basic concepts of data science concerning data analysis and machine learning
Description
Wildfires represent a significant threat to ecosystems, communities, and infrastructure worldwide. Accurately predicting the probability of human-caused ignitions is crucial for effective wildfire management and prevention strategies. This research aims to develop advanced machine-learning models to estimate the likelihood of human-caused wildfire ignition using a combination of geospatial, socioeconomic, and environmental data.
The study will build upon existing work in wildfire ignition prediction, specifically focusing on improving the accuracy and interpretability of human-caused ignition models. This research aims to create more precise and actionable ignition probability maps by leveraging high-resolution geospatial data, socioeconomic factors, and state-of-the-art machine-learning techniques.
Key aspects of the research include:
- Developing and comparing various machine learning models for ignition probability prediction.
- Integrating diverse geospatial datasets to capture human activity patterns and environmental conditions.
- Exploring the use of temporal data to account for seasonal and long-term trends in ignition patterns.
- Investigating the interpretability of machine learning models to understand key factors influencing ignition probability.