AN OPEN RISK INDEX WITH LEARNING INDICATORS FROM OSM-TAGS, DEVELOPED BY MACHINE LEARNING AND TRAINED WITH THE WORLDRISKINDEX

Feldmeyer, D.; Sauter, H.; Birkmann, J.

While climate change is already a real issue in many parts of the world, it is even more threatening the well-being of future generations. The SDG 1.5 explicitly aims to reduce the vulnerability and exposure to climate related hazards by 2030. TheWorld Risk Index (WRI) is one well-respected approach in profiling countries risk to natural hazard. To effectively monitor development and detect decision points on the climate resilience pathway, data of high resolution in space and time about the world’s countries is of urgent importance. The World Risk Index will guide the supervised learning part resulting in an indicator set derived from OpenStreetMap (OSM) tags, establishing on one hand an open risk index and adding deep explanatory power to its components by a qualitative discussion of the OSM themes. The second part explores with unsupervised algorithms the inherent characteristic of country groups classified by the open risk index and deduces common patterns of socio-economic vulnerability. Hence, the inherent challenge of this work is to substitute existing static indicators with new dynamic indicators, not only substituting them but also painting a more detailed picture. Moreover, new data sources still questioned often by their reliability compared to World Bank or census data, and therefore its opportunities are neglected instead of critically exploring the potential. This unique combination is not done yet and bares huge potential moreover united with the open source geo community to contribute a little piece of the puzzle for achieving the SDG 1.5.

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Zitierform:

Feldmeyer, D. / Sauter, H. / Birkmann, J.: AN OPEN RISK INDEX WITH LEARNING INDICATORS FROM OSM-TAGS, DEVELOPED BY MACHINE LEARNING AND TRAINED WITH THE WORLDRISKINDEX. 2019. Copernicus Publications.

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