Zhangziman Song
Zhangziman Song is a Data Scientist in the Sustainability Software Division at IBM. She's worked on building ML models using weather data for six years. She’s passionate about using AI and ML to enable businesses to prepare better for adverse weather conditions. More recently, she is working on AI models using other geospatial datasets such as satellite imagery and Lidar.
Sessions
The rarity and diversity of weather events and the large range of impacts of these events presents unique challenges in various phases of model building – feature engineering, model training, model evaluation and model selection. We discuss best in class approaches to optimize all relevant parameters and continuously improve model performance to deliver accurate actionable results via a highly scalable ML operational environment, enabling them to mitigate effects of climate change. We describe the challenges and approach using the Outage Prediction use case for Utility companies. These companies spend billions of dollars every year restoring power outages, majority of which are weather related. Climate change is creating more frequent and longer lasting power outages and making it harder to predict everyday weather events. Our approach has been used successfully in predicting weather caused outages that are then used to proactively mobilize the power restoration process.