Next Article in Journal
TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR
Next Article in Special Issue
Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning
Previous Article in Journal
Exploration of Brain Connectivity during Human Inhibitory Control Using Inter-Trial Coherence
Previous Article in Special Issue
Low-Cost GNSS Solution for Continuous Monitoring of Slope Instabilities Applied to Madonna Del Sasso Sanctuary (NW Italy)
Open AccessArticle

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

1
Department of Civil Engineering, Kermanshah University of Technology, 6715685420 Kermanshah, Iran
2
The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
3
Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209 Neungdong-ro Gwangjin-gu, Seoul 05006, Korea
4
Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
6
Department of Geology & Geophysics, College of Science, King Saud Univ., P.O. Box 2455, Riyadh 11451, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1723; https://doi.org/10.3390/s20061723
Received: 25 February 2020 / Revised: 9 March 2020 / Accepted: 17 March 2020 / Published: 19 March 2020
Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area. View Full-Text
Keywords: landslide susceptibility; GIS; remote sensing; ANFIS; metaheuristic optimization landslide susceptibility; GIS; remote sensing; ANFIS; metaheuristic optimization
Show Figures

Graphical abstract

MDPI and ACS Style

Mehrabi, M.; Pradhan, B.; Moayedi, H.; Alamri, A. Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques. Sensors 2020, 20, 1723.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop