Volume 13 Issue 5
Sep.  2022
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Kashif Ullah, Yi Wang, Zhice Fang, Lizhe Wang, Mahfuzur Rahman. Multi-hazard susceptibility mapping based on Convolutional Neural Networks[J]. Geoscience Frontiers, 2022, 13(5): 101425. doi: 10.1016/j.gsf.2022.101425
Citation: Kashif Ullah, Yi Wang, Zhice Fang, Lizhe Wang, Mahfuzur Rahman. Multi-hazard susceptibility mapping based on Convolutional Neural Networks[J]. Geoscience Frontiers, 2022, 13(5): 101425. doi: 10.1016/j.gsf.2022.101425

Multi-hazard susceptibility mapping based on Convolutional Neural Networks

doi: 10.1016/j.gsf.2022.101425

This work was supported by the Joint Funds of the National Natural Science Foundation of China (U21A2013), the State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences (GBL12107) and the National Natural Science Foundation of China (61271408). The authors would like to thank Latif and Sardar Wajid for helping in field visit and data collection (landslide and debris flow points). The authors would also like to thank the Associate Editor Prof. Biswajeet Pradhan, and three anonymous reviewers for their constructive comments, which improved the manuscript.

  • Received Date: 2022-01-27
  • Accepted Date: 2022-06-15
  • Rev Recd Date: 2022-05-05
  • Publish Date: 2022-06-17
  • Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.
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