TY  - JOUR
PY  - 2020//
TI  - Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning
JO  - Science of the total environment
A1  - Dou, Jie
A1  - Yunus, Ali P.
A1  - Merghadi, Abdelaziz
A1  - Shirzadi, Ataollah
A1  - Nguyen, Hoang
A1  - Hussain, Yawar
A1  - Avtar, Ram
A1  - Chen, Yulong
A1  - Pham, Binh Thai
A1  - Yamagishi, Hiromitsu
SP  - e137320
EP  - e137320
VL  - 720
IS  - 
N2  - Predictive capability of landslide susceptibilities is assumed to be varied with different sampling techniques, such as (a) the landslide scarp centroid, (b) centroid of landslide body, (c) samples of the scrap region representing the scarp polygon, and (d) samples of the landslide body representing the entire landslide body. However, new advancements in statistical and machine learning algorithms continuously being updated the landslide susceptibility paradigm. This paper explores the predictive performance power of different sampling techniques in landslide susceptibility mapping in the wake of increased usage of artificial intelligence. We used logistic regression (LR), neural network (NNET), and deep learning neural network (DNN) model for testing and validation of the models. The tests were applied to the 2018 Hokkaido Earthquake affected areas using a set of 11 predictor variables (seismic, topographic, and hydrological). We found that the prediction rates are inconsequential with the DNN model irrespective of the sampling technique (AUC: 0.904 - 0.919). Whereas, testing with LR (AUC: 0.825 - 0.785) and NNET (AUC: 0.882 - 0.858) produces larger differences in the accuracies between the four datasets. Nonetheless, the highest success rates were obtained for samples within the landslide scarp area. The analogy was then validated with a published landslide inventory from the 2015 Gorkha earthquake. We, therefore, suggest that DNN models as an appropriate technique to increase the predictive performance of landslide susceptibilities if the landslide scarp and body are not characterized properly in an inventory.<br><br>Copyright © 2020 Elsevier B.V. All rights reserved.<p />  <p>Language: en</p>
LA  - en
SN  - 0048-9697
UR  - http://dx.doi.org/10.1016/j.scitotenv.2020.137320
ID  - ref1
ER  -