Emergency Response Prediction with Time Series machine Learning Model
Responsible for 17 Bio-Rad locations (Hercules, Richmond, Benicia, Pleasanton, and Santa Rosa) in Northern California, with over 150 Emergency Response volunteers from director to entry-level that are inspired and committed to responding to both chemical and medical emergencies across the 17 locations with the primary objective to create a safe environment for over 4,000 employees in NorCal Sites.
Proactive Approach on Emergency Response
Developed emergency response readiness plan based on time-series emergency response predictions using historical emergency records.
- Predicted emergency events using timeseries forecast and prep ERT responders on anticipated emergency.
- Determine the emergency frequency distribution and peak periods.
- Identified association between apparent temperature change and type of emergency events.
- Identified type emergency that occurred during last day of the week and holiday weekend.
- Identified fatigue influenced emergencies.
Unable to share prediction analysis and visualization due to internal process constraints, we were able to proactively respond to emergencies and provide the best customer emergency response support for the NorCal employees at the 17 locations.
Quality emergency support was achieved as a result of predictive analytics models designed to assess historical data, discover patterns, observe trends, and use that information to predict the future.
Experts have suggested that data-driven organizations that utilize predictive analytical models not only make better strategic decisions, but also enjoy higher operational efficiency, improved customer satisfaction, along with robust profit and revenue levels.
Building a strong working relationship with City Fire department
Built strong working relations with the city fire department that reduced their response time to our facilities. Their rapid responses helped save lives. Our strong working relationship with fire enabled the fire department to be fully supportive and provided real-life experience information sharing during our drills or big company outdoor events.
