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.

此研究针对加拿大埃德蒙顿市的各类紧急事件，通过数据收集、描述性分析、特征分析与选择以及建立预测模型，以不同时间和空间分辨率预测各类事件的发生概率，并对其与社会经济和人口数据的关联进行分析，结果显示模型在大部分事件类型上表现良好，可用于紧急救援服务部门的资源分配和规划，同时研究还发现 COVID-19 对事件发生和预测模型的精度有显著影响。

用于预测火灾和其他紧急事件的统计和机器学习模型

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

The electricity industry is heavily implementing smart grid technologies to
improve reliability, availability, security, and efficiency. This
implementation needs technological advancements, the development of standards
and regulations, as well as testing and planning. Smart grid load forecasting
and management are critical for reducing demand volatility and improving the
market mechanism that connects generators, distributors, and retailers. During
policy implementations or external interventions, it is necessary to analyse
the uncertainty of their impact on the electricity demand to enable a more
accurate response of the system to fluctuating demand. This paper analyses the
uncertainties of external intervention impacts on electricity demand. It
implements a framework that combines probabilistic and global forecasting
models using a deep learning approach to estimate the causal impact
distribution of an intervention. The causal effect is assessed by predicting
the counterfactual distribution outcome for the affected instances and then
contrasting it to the real outcomes. We consider the impact of Covid-19
lockdowns on energy usage as a case study to evaluate the non-uniform effect of
this intervention on the electricity demand distribution. We could show that
during the initial lockdowns in Australia and some European countries, there
was often a more significant decrease in the troughs than in the peaks, while
the mean remained almost unaffected.

本文通过对智能电网进行技术创新、标准开发、规范实施、测试规划等方面的探讨，运用基于深度学习方法的概率和全局预测模型，分析了外部干预对电力需求的不确定性及其影响情况，以新冠肺炎疫情对电力需求的影响为例，证明在短期内干预的峰谷差异是相当显著的。