Nhu, Viet Ha and Mohammadi, Ayub and Shahabi, Himan and Ahmad, Baharin and Al-Ansari, Nadhir and Shirzadi, Ataollah and Clague, John J. and Jaafari, Abolfazl and Chen, Wei and Nguyen, Hoang (2020) Landslide susceptibility mapping using machine learning algorithms and remote sensing data in a tropical environment. International Journal of Environmental Research and Public Health, 17 (14). pp. 1-23. ISSN 1661-7827
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Official URL: http://dx.doi.org/10.3390/ijerph17144933
Abstract
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble ABADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and landuse managers to mitigate landslide hazards.
Item Type: | Article |
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Uncontrolled Keywords: | Cameron Highlands, ensemble model, machine learning, Malaysia |
Subjects: | N Fine Arts > NA Architecture |
Divisions: | Built Environment |
ID Code: | 93344 |
Deposited By: | Yanti Mohd Shah |
Deposited On: | 19 Nov 2021 03:30 |
Last Modified: | 19 Nov 2021 03:30 |
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