Potential non-disasters of 2021

Author's accepted manuscript.This author accepted manuscript is deposited under a Creative Commons Attribution Non-commercial 4.0 International (CC BY-NC) licence. This means that anyone may distribute, adapt, and build upon the work for non-commercial purposes, subject to full attribution. If you wish to use this manuscript for commercial purposes, please contact [email protected]: This short paper compiles some potential disasters that might not have happened in 2021 even though a major hazard occurred. No definitive statements are made of what did or did not transpire in each instance. Instead, the material offers a pedagogical and communications approach, especially to encourage deeper investigation and critique into what are and are not labelled as disasters and non-disasters—and the consequences of this labelling. Design/methodology/approach: This short paper adopts a subjective approach to describing situations in 2021 in which a hazard was evident, but a disaster might not have resulted. Brief explanations are provided with some evidence and reasoning, to be used in teaching and science communication for deeper examination, verification and critique. Findings: Examples exist in which hazards could have become disasters, but disasters might not have manifested, ostensibly due to disaster risk reduction. Reaching firm conclusions about so-called “non-disasters” is less straightforward. Originality/value: Many reports rank the seemingly worst disasters while research often compares a disaster investigated with the apparently worst disasters previously experienced. This short paper instead provides possible ways of teaching and communicating potential non-disasters. It offers an approach for applying lessons to encourage action on disaster risk reduction, while recognising challenges with the labels “non-disaster”, “success” and “positive news”.publishedVersio

Applying Machine Learning Models to First Responder Collisions Beside Roads: Insights from “Two Vehicles Hit a Parked Motor Vehicle” Data

First responders including firefighters, paramedics, and police officers are among the first to respond to vehicle collisions on roads and highways. Police officers conduct regular roadside Please check if the country name is correct traffic controls and checks on urban and rural roads, and highways. Once first responders begin such operations, they are vulnerable to motor vehicle collisions by oncoming traffic, a circumstance that calls for a better understanding of contributing factors and the extent to which they affect tragic outcomes. In light of factors identified in the literature, this paper applies machine learning methods including decision tree and random forest to a subset of the National Collision Database (NCDB) of Canada that includes information on collisions between two vehicles (one in parked position) and the severity of these collisions as measured by having or not having injuries. Findings reveal that key measurable, predictable, and sensible factors such as time, location, and weather conditions, as well as the interconnections among them, can explain the severity of collisions that may happen between motor vehicles and first responders who are working alongside the roads. Analysis from longitudinal data is rich and the use of automated methods can be used to predict and assess the risk and vulnerability of first responders while responding to or operating on different roads and conditions