Landslide Susceptibility Mapping with Deep Learning Algorithms

Jules Maurice Habumugisha, Ningsheng Chen, Mahfuzur Rahman, Md Monirul Islam, Hilal Ahmad, Ahmed Elbeltagi, Gitika Sharma, Sharmina Naznin Liza and Ashraf Dewan
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Jules Maurice Habumugisha: Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu 610041, China
Ningsheng Chen: Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu 610041, China
Mahfuzur Rahman: Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh
Md Monirul Islam: Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh
Hilal Ahmad: School of Civil and Resource Engineering, University of Science and Technology, Beijing 100083, China
Ahmed Elbeltagi: Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt
Gitika Sharma: Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India
Sharmina Naznin Liza: Department of Civil Engineering, Dhaka University of Engineering & Technology (DUET), Gazipur 1707, Bangladesh
Ashraf Dewan: School of Earth and Planetary Sciences, Curtin University, Bentley, WA 6102, Australia

Sustainability, 2022, vol. 14, issue 3, 1-22

Abstract: Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.

Keywords: landslides; deep learning algorithm; geographic information system; Sichuan; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (8)

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