Summary of Statistical and Machine Learning Models For Predicting Fire and Other Emergency Events, by Dilli Prasad Sharma et al.
Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events
by Dilli Prasad Sharma, Nasim Beigi-Mohammadi, Hongxiang Geng, Dawn Dixon, Rob Madro, Phil Emmenegger, Carlos Tobar, Jeff Li, Alberto Leon-Garcia
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a systematic approach to developing predictive models for various types of emergency events in Edmonton, Canada. The authors collect data, perform descriptive analysis, feature selection, and develop negative binomial regression models for each event type at different temporal and spatial resolutions. They evaluate the models’ performance at neighborhood and fire station service area levels, finding acceptable prediction errors for weekly and monthly periods. The study also examines the impact of the COVID-19 pandemic on emergency event occurrences and model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Predictive models can help emergency services prepare for and mitigate the consequences of emergency events. Researchers developed predictive models for different types of emergencies in Edmonton, Canada. They collected data, analyzed it to identify important features, and created models using negative binomial regression. The models were tested at neighborhood and fire station levels and showed promising results. |
Keywords
* Artificial intelligence * Feature selection * Regression
