Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events

• URL: http://arxiv.org/abs/2402.09553v1
• Date: Wed, 14 Feb 2024 20:10:30 GMT
• Title: Statistical and Machine Learning Models for Predicting Fire and Other Emergency Events
• Authors: Dilli Prasad Sharma, Nasim Beigi-Mohammadi, Hongxiang Geng, Dawn Dixon, Rob Madro, Phil Emmenegger, Carlos Tobar, Jeff Li, Alberto Leon-Garcia
• Abstract summary: We present a systematic development of predictive models for various types of emergency events in the City of Edmonton, Canada. We analyze the association of event types with socioeconomic and demographic data at the neighborhood level. We examine the impact of the COVID-19 pandemic on the occurrence of events and on the accuracy of event predictor models.
• Score: 1.216599520489317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Emergency events in a city cause considerable economic loss to individuals, their families, and the community. Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events. In this paper, we present a systematic development of predictive models for various types of emergency events in the City of Edmonton, Canada. We present methods for (i) data collection and dataset development; (ii) descriptive analysis of each event type and its characteristics at different spatiotemporal levels; (iii) feature analysis and selection based on correlation coefficient analysis and feature importance analysis; and (iv) development of prediction models for the likelihood of occurrence of each event type at different temporal and spatial resolutions. We analyze the association of event types with socioeconomic and demographic data at the neighborhood level, identify a set of predictors for each event type, and develop predictive models with negative binomial regression. We conduct evaluations at neighborhood and fire station service area levels. Our results show that the models perform well for most of the event types with acceptable prediction errors for weekly and monthly periods. The evaluation shows that the prediction accuracy is consistent at the level of the fire station, so the predictions can be used in management by fire rescue service departments for planning resource allocation for these time periods. We also examine the impact of the COVID-19 pandemic on the occurrence of events and on the accuracy of event predictor models. Our findings show that COVID-19 had a significant impact on the performance of the event prediction models.

Related papers

• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• CEP3: Community Event Prediction with Neural Point Process on Graph [59.434777403325604]
  We propose a novel model combining Graph Neural Networks and Marked Temporal Point Process (MTPP) Our experiments demonstrate the superior performance of our model in terms of both model accuracy and training efficiency.
  arXiv  Detail & Related papers  (2022-05-21T15:30:25Z)
• KnitCity: a machine learning-based, game-theoretical framework for prediction assessment and seismic risk policy design [0.0]
  We introduce a framework that allows to design, evaluate and compare not only predictors but also decision-making policies. We construct efficient policies using a reinforcement learning environment and various time-series predictors based on artificial neural networks.
  arXiv  Detail & Related papers  (2022-05-05T14:38:03Z)
• Causal Knowledge Guided Societal Event Forecasting [24.437437565689393]
  We introduce a deep learning framework that integrates causal effect estimation into event forecasting. Two robust learning modules, including a feature reweighting module and an approximate loss, are introduced to enable prior knowledge injection.
  arXiv  Detail & Related papers  (2021-12-10T17:41:02Z)
• Explainable Event Recognition [5.103059984821972]
  We propose an explainable event recognition framework relying on Grad-CAM and an Xception architecture-based CNN model. Experiments are conducted on three large-scale datasets covering a diversified set of natural disasters, social, and sports events.
  arXiv  Detail & Related papers  (2021-10-02T08:40:33Z)
• A model for traffic incident prediction using emergency braking data [77.34726150561087]
  We address the fundamental problem of data scarcity in road traffic accident prediction by training our model on emergency braking events instead of accidents. We present a prototype implementing a traffic incident prediction model for Germany based on emergency braking data from Mercedes-Benz vehicles.
  arXiv  Detail & Related papers  (2021-02-12T18:17:12Z)
• Forecasting COVID-19 daily cases using phone call data [0.0]
  We propose a simple Multiple Linear Regression model optimised to use call data to forecast the number of daily confirmed cases. Our proposed approach outperforms ARIMA, ETS and a regression model without call data, evaluated by three point forecast error metrics, one prediction interval and two probabilistic forecast accuracy measures.
  arXiv  Detail & Related papers  (2020-10-05T18:07:07Z)
• Backtesting the predictability of COVID-19 [0.0]
  We use historical data of COVID-19 infections from 253 regions from the period of 22nd January 2020 until 22nd June 2020. Prediction errors are substantially higher in early stages of the pandemic, resulting from limited data. The more confirmed cases a country exhibits at any point in time, the lower the error in forecasting future confirmed cases.
  arXiv  Detail & Related papers  (2020-07-22T13:18:00Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.