TY  - JOUR
PY  - 2018//
TI  - Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping
JO  - Sensors (Basel)
A1  - Shirzadi, Ataollah
A1  - Soliamani, Karim
A1  - Habibnejhad, Mahmood
A1  - Kavian, Ataollah
A1  - Chapi, Kamran
A1  - Shahabi, Himan
A1  - Chen, Wei
A1  - Khosravi, Khabat
A1  - Thai Pham, Binh
A1  - Pradhan, Biswajeet
A1  - Ahmad, Anuar
A1  - Bin Ahmad, Baharin
A1  - Tien Bui, Dieu
SP  - s18113777
EP  - s18113777
VL  - 18
IS  - 11
N2  - The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). <br><br>RESULTS show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s18113777
ID  - ref1
ER  -