A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Dieu Tien Bui; Hossein Moayedi; Bahareh Kalantar; Abdolreza Osouli; Biswajeet Pradhan; Hoang Nguyen; Ahmad Safuan A Rashid

doi:10.3390/s19163590

A Novel Swarm Intelligence-Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

Sensors (Basel). 2019 Aug 17;19(16):3590. doi: 10.3390/s19163590.

Authors

Dieu Tien Bui¹, Hossein Moayedi^{2

3}, Bahareh Kalantar⁴, Abdolreza Osouli⁵, Biswajeet Pradhan^{6

7}, Hoang Nguyen^{8

9}, Ahmad Safuan A Rashid¹⁰

Affiliations

¹ Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
² Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. hossein.moayedi@tdtu.edu.vn.
³ Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam. hossein.moayedi@tdtu.edu.vn.
⁴ RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan.
⁵ Civil Engineering Department, Southern Illinois University, Edwardsville, IL 62026, USA.
⁶ Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia.
⁷ Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea.
⁸ Department of Surface Mining, Hanoi University of Mining land Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam.
⁹ Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien Street, Duc Thang Ward, Bac Tu Liem District, Hanoi, Vietnam.
¹⁰ Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia.

Abstract

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROC_ANN = 0.731 and AUROC_HHO-ANN = 0.777) and predicting (AUROC_ANN = 0.720 and AUROC_HHO-ANN = 0.773) the landslide pattern.

Keywords: GIS; Harris hawks optimization; artificial neural network; landslide susceptibility mapping.