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| Titel |
Optimal estimator for assessing landslide model performance |
| VerfasserIn |
J. C. Huang, S. J. Kao |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 10, no. 6 ; Nr. 10, no. 6 (2006-12-14), S.957-965 |
| Datensatznummer |
250008295
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| Publikation (Nr.) |
 copernicus.org/hess-10-957-2006.pdf |
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| Zusammenfassung |
| The commonly used success rate (SR) in evaluating cell-based landslide model
performance is based on the ratio of successfully predicted landslide sites
over total actual landslide sites without considering the performance in
predicting stable cells. We proposed a modified SR (MSR), in which the
performance of stable cell prediction is included. The advantage of MSR is
to avoid over- and under-prediction while upholding the stable sensitivity
throughout all simulated cases. Stochastic analyses are conducted by using
artificial landslide maps and simulations with a full range of performances
(from worst to perfect) in both stable and unstable cell predictions.
Stochastic analyses reveal mathematical responses of estimators to various
model results in calculating performance. The Kappa method, which is
commonly used for satellite image analysis, is improper for landslide
modeling giving inconsistent performance when landslide coverage changes. To
examine differences among SR and MSR in real model application, we applied
the SHALSTAB model onto a mountainous watershed in Taiwan. Case study shows
that stable and unstable cell predictions are inter-exclusive in SHALSTAB
model. The optimal estimator should compromise landslide over- and
under-prediction. According to our 4000 simulations, the best simulation
generated by MSR projects 83 hits over 131 actual landslide sites while the
unstable cells cover only 16% of the studied watershed. By contrast,
despite the fact that the best simulation deduced from SR projects 120 hits
over 131 actual landslide sites, this high performance is only obtained when
unstable cells cover an incredibly high landslide cover (~75%) of
the entire watershed exhibiting a significant landslide over-prediction. |
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