Data Integration for the Development of a Seismic Loss Prediction Model for Residential Buildings in New Zealand
| Autor: | Samuel Roeslin, Liam Wotherspoon, Quincy Ma, Joerg Wicker |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies Government Earthquake engineering Geographic information system business.industry Process (engineering) Environmental resource management 0211 other engineering and technologies 02 engineering and technology Commission 010502 geochemistry & geophysics computer.software_genre 01 natural sciences Geography Scale (social sciences) business Categorical variable computer 0105 earth and related environmental sciences Data integration |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030438869 PKDD/ECML Workshops (2) |
| Popis: | In 2010–2011, New Zealand experienced the most damaging earthquakes in its history. It led to extensive damage to Christchurch buildings, infrastructure and its surroundings; affecting commercial and residential buildings. The direct economic losses represented 20% of New Zealand’s GDP in 2011. Owing to New Zealand’s particular insurance structure, the insurance sector contributed to over 80% of losses for a total of more than NZ$31 billion. Amongst this, over NZ$11 billion of the losses arose from residential building claims and were covered either partially or entirely from the NZ government backed Earthquake Commission (EQC) cover insurance scheme. In the process of resolving the claims, EQC collected detailed financial loss data, post-event observations and building characteristics for each of the approximately 434,000 claims lodged following the Canterbury Earthquake sequence (CES). Added to this, the active NZ earthquake engineering community treated the event as a large scale outdoor experiment and collected extensive data on the ground shaking levels, soil conditions, and liquefaction occurrence throughout wider Christchurch. This paper discusses the necessary data preparation process preceding the development of a machine learning seismic loss model. The process draws heavily upon using Geographic Information System (GIS) techniques to aggregate relevant information from multiple databases interpolating data between categories and converting data between continuous and categorical forms. Subsequently, the database is processed, and a residential seismic loss prediction model is developed using machine learning. The aim is to develop a ‘grey-box’ model enabling human interpretability of the decision steps. |
| Databáze: | OpenAIRE |
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