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Classification of GIS-based models according to natural hazard types

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Abstract

Natural disaster and human-evoked disasters can’t be clearly distinguished in these days. From monitoring, alerting, managing, responding and recovering, all the experts including scientists, policy makers, and software engineers have discussed what will happen, what should be done first, what impacts the disaster events produce on economy and societies. At this moment, experts in each domain would like to apply their scientific models to explain the phenomena of potential disaster events and to estimate the human casualty and economic and social losses. What is model? Do we need data to make a model? How in detail data are enough to build and verified models? This study aims to overcome the difference in understanding natural hazard-related models and to make research and development program on hazard estimation model which is applicable to real worlds with least mismatch or less gap in understanding the concept of model. Here we suggest classification of the level of applications based on the purpose of each model, regardless of disaster types. Classifications were applied to researches and programs on flood, drought, earthquake and volcanic disasters.

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Notes

  1. Fourth-generation language is designed to be closer to natural language than assembler language which requires a considerable amount of programming knowledge.

  2. WAMIS (www.wamis.go.kr) system gives real-time water level data and basic GIS data for runoff data each basin.

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Acknowledgments

This work was supported by a Grant (MPSS-NH-2015-81) through the Natural Hazard Mitigation Research Group funded by Ministry of Public Safety and Security of Korean Government.

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Authors and Affiliations

  1. Ziinconsulting Inc., Seoul, Korea

    Eunmi Chang

  2. Department of Geography, Sungshin Women’s University, Seoul, Korea

    Kyeong Park

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  1. Eunmi Chang
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Correspondence to Kyeong Park.

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Chang, E., Park, K. Classification of GIS-based models according to natural hazard types. Spat. Inf. Res. 24, 103–114 (2016). https://doi.org/10.1007/s41324-016-0012-3

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  • DOI: https://doi.org/10.1007/s41324-016-0012-3

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