Convolutional Neural Network-Optimized Moth Flame Algorithm for Shallow Landslide Susceptible Analysis

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 C...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.32727-32736
Main Authors: Pham, Vu Dong, Nguyen, Quoc-Huy, Nguyen, Huu-Duy, Pham, Van-Manh, Vu, Van Manh, Bui, Quang-Thanh
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Convolutional neural network
Disaster management
Emergency preparedness
Fires
Flash floods
Indexes
landslide susceptibility
Landslides
Landslides & mudslides
meta-heuristic algorithm
moth flame optimization algorithm
Neural networks
Optimization
Regression analysis
Rivers
Statistical analysis
Statistical tests
Swarm intelligence
Terrain factors
Three dimensional models
Training
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Summary: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 × 7 height ×12 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 = 0.3685, Mean absolute error = 0.2888, Area under Receiver characteristic curve = 0.889 and Overall accuracy = 80.1056%) 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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2973415