Journal
IEEE ACCESS
Volume 8, Issue -, Pages 32727-32736Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2973415
Keywords
Terrain factors; Rivers; Indexes; Optimization; Training; Fires; Convolutional neural network; meta-heuristic algorithm; moth flame optimization algorithm; landslide susceptibility
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Funding
- Asia Research Center, Vietnam National University - Hanoi
- Korea Foundation for Advanced Studies [CA.19.8A]
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Convolutional neural network (CNN) is a widely used method in solving classification and regression applications in industries, engineering, and science. This study investigates the optimizing capability of a swarm intelligence algorithm named moth flame optimizer (MFO) for the optimal search of a CNN hyper-parameters (values of filters) and weights of fully connected layers. The proposed model was run with a 3-dimensional dataset (7 width height depth), which was constructed through including seven neighbor pixels (vertically and horizontally) from landslide location and 12 predictor variables. Muong Te district, Lai Chau province, Vietnam was selected as the case study, as it had recently undergone severe impacts of landslides and flash floods. The performance of this proposed model was compared with conventional classifiers, i.e., Random forest, Random subspace, and CNN-optimized Adaptive gradient descend, by using standard metrics. The results showed that the CNN-optimized MFO (Root mean square error & x003D; 0.3685, Mean absolute error & x003D; 0.2888, Area under Receiver characteristic curve & x003D; 0.889 and Overall accuracy & x003D; 80.1056 & x0025;) outperformed the benchmarked methods in all comparing indicators. Besides, the statistical test of difference was also carried out by using the Wilcoxon signed ranked test for non-parametric variables. With these statistical measurements, the proposed model could be used as an alternative solution for landslide susceptibility mapping to support local disaster preparedness plans.
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