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Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events

Citation

Pilkington, Stephanie; Mahmoud, Hussam (2022), Data from: Applicability of artificial neural networks to integrate socio-technical drivers of buildings recovery following extreme wind events, Dryad, Dataset, https://doi.org/10.5061/dryad.9kd51c5jb

Abstract

The data provided and the associated MATLAB code were used to build an Artificial Neural Network Model to capture the reconstruction (recovery) of various buildings subjected to tornado events in the State of Missouri. The ANN model utilizes relevant tornado, societal demographic, and structural data to determine a building’s resulting damage state from an extreme wind event and the subsequent recovery time. Abstract for the publication is as follows:

In a companion article, previously published in Royal Society Open Science, the authors used Graph Theory to evaluate artificial neural network models for potential social and building variables interactions contributing to building wind damage. The results promisingly highlighted the importance of social variables in modeling damage as opposed to the traditional approach of solely considering physical characteristics of a building. Within this update article, the same methods are used to evaluate two different artificial neural networks for modelling building repair and/or rebuild (recovery) time. In contrast to the damage models, the recovery models consider (A) primarily social variables and then (B) introduce structural variables. These two models are then evaluated using centrality and shortest path concepts of Graph Theory as well as validated against data from the 2011 Joplin Tornado. The results of this analysis do not show the same distinctions as were found in the analysis of the damage models from the companion article. The overarching lack of discernible and consistent differences in the recovery models suggests that social variables that drive damage are not necessarily contributions to recovery. The differences also serve to reinforce that machine learning methods are best used when the contributing variables are already well understood.

Methods

Social demographic data was collected from U.S. Census American Community Survey (ACS) at the block group level. Building characteristics, such as material and height, were visually evaluated from NWS Damage Survey Viewer photos from damage assessments conducted from January 1, 2011 to December 31, 2015. For this research it was determined that an individual building would belocated within a census block group and would assume the characteristics of that group. Each NWS damage photo was previously geo-tagged. This location was subsequently found using Google Earth, which possesses an ability to step back through satellite images over time. Time steps through Google Earth were used to conduct a visual assessment of the structure prior to the hazard event up to 3 years following the event, provided there were documented satelitte images for that time. Each structure’s condition was assessed for whether it was rebuilt and reoccupied by 6 months, 1 year, 1.5 years, 2 years,or not yet recovered by 2 years.