Natural disasters like floods and droughts pose significant threats to communities worldwide, making accurate and timely forecasting essential for mitigation and response. This presentation will delve into the development and implementation of AI-based models for natural disaster forecasting, with a specific focus on floods and droughts.
We will explore Google's machine learning-driven hydrologic model for riverine flood forecasting, which has been operational for several years and provides predictions up to seven days in advance. Additionally, we will discuss a flash flood model currently in development. The talk will also cover a machine learning model for drought forecasting, which provides predictions at a three-month lead time.
A key focus of this discussion will be the robust evaluation of these models. We'll examine various methods for assessing their skill, including comparisons against historical data, satellite observations, and established performance benchmarks across different regions. This presentation will highlight how advanced AI can enhance our ability to predict and prepare for natural disasters, ultimately supporting global resilience efforts.