Abstract
Modeling of a complex environment is inevitably associated with uncertainties arising from the model design or data errors. In the uncertainty assessment, the bias (related to accuracy) and the random error (related to precision) are distinguished. Recent reviews of case studies, which used data-driven methods for landslide susceptibility assessment (LSA), indicate a general lack of appropriate evaluation of uncertainties. In this paper, we discuss practical techniques to account for uncertainties in LSA, relying majorly on the examples from the project “Landslide Hazard and Risk Assessment for Lanzhou” (LHARA).
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Acknowledgements
We conducted this work in the framework of a scientific-technical cooperation project between the Federal Institute for Geosciences and Natural Resources (BGR) and the China Geological Survey (CGS) co-funded by the German Ministry of the Economic Affairs and Energy (BMWi) and Ministry of Natural Resources of the People’s Republik of China.
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Torizin, J., Fuchs, M., Kuhn, D., Balzer, D., Wang, L. (2021). Practical Accounting for Uncertainties in Data-Driven Landslide Susceptibility Models. Examples from the Lanzhou Case Study. In: Guzzetti, F., Mihalić Arbanas, S., Reichenbach, P., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60227-7_27
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DOI: https://doi.org/10.1007/978-3-030-60227-7_27
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