Multi-hazard susceptibility mapping based on Convolutional Neural Networks |
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Affiliation: | 1. Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China;2. State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430074, China;3. School of Computer Science, China University of Geosciences, Wuhan 430074, China;4. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China;5. Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka 1230, Bangladesh |
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Abstract: | 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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Keywords: | Multi-hazard Convolutional Neural Network Machine learning Eastern Hindukush Pakistan LSM" },{" #name" :" keyword" ," $" :{" id" :" k0035" }," $$" :[{" #name" :" text" ," _" :" Landslide Susceptibility Mapping FFSM" },{" #name" :" keyword" ," $" :{" id" :" k0045" }," $$" :[{" #name" :" text" ," _" :" Flash Flood Susceptibility Mapping DFSM" },{" #name" :" keyword" ," $" :{" id" :" k0055" }," $$" :[{" #name" :" text" ," _" :" Debris Flow Susceptibility Mapping CNN" },{" #name" :" keyword" ," $" :{" id" :" k0065" }," $$" :[{" #name" :" text" ," _" :" Convolutional Neural Network DL" },{" #name" :" keyword" ," $" :{" id" :" k0075" }," $$" :[{" #name" :" text" ," _" :" Deep Learning MCDM" },{" #name" :" keyword" ," $" :{" id" :" k0085" }," $$" :[{" #name" :" text" ," _" :" Multi-Criteria Decision-Making ML" },{" #name" :" keyword" ," $" :{" id" :" k0095" }," $$" :[{" #name" :" text" ," _" :" Machine Learning LR" },{" #name" :" keyword" ," $" :{" id" :" k0105" }," $$" :[{" #name" :" text" ," _" :" Logistic Regression KNN" },{" #name" :" keyword" ," $" :{" id" :" k0115" }," $$" :[{" #name" :" text" ," _" :" K-Nearest Neighbor SPI" },{" #name" :" keyword" ," $" :{" id" :" k0125" }," $$" :[{" #name" :" text" ," _" :" Stream Power Index NDVI" },{" #name" :" keyword" ," $" :{" id" :" k0135" }," $$" :[{" #name" :" text" ," _" :" Normalized Difference Vegetation Index STI" },{" #name" :" keyword" ," $" :{" id" :" k0145" }," $$" :[{" #name" :" text" ," _" :" Sediment Transportation Index NDMA" },{" #name" :" keyword" ," $" :{" id" :" k0155" }," $$" :[{" #name" :" text" ," _" :" National Disaster Management Authority TWI" },{" #name" :" keyword" ," $" :{" id" :" k0165" }," $$" :[{" #name" :" text" ," _" :" Topographic Wetness Index ALOS" },{" #name" :" keyword" ," $" :{" id" :" k0175" }," $$" :[{" #name" :" text" ," _" :" Advanced Land Observing Satellite DEM" },{" #name" :" keyword" ," $" :{" id" :" k0185" }," $$" :[{" #name" :" text" ," _" :" Digital Elevation Model AUC" },{" #name" :" keyword" ," $" :{" id" :" k0195" }," $$" :[{" #name" :" text" ," _" :" Area Under the Receiver Operating Characteristic Curve OA" },{" #name" :" keyword" ," $" :{" id" :" k0205" }," $$" :[{" #name" :" text" ," _" :" Overall Accuracy MAE" },{" #name" :" keyword" ," $" :{" id" :" k0215" }," $$" :[{" #name" :" text" ," _" :" Mean Absolute Error RMSE" },{" #name" :" keyword" ," $" :{" id" :" k0225" }," $$" :[{" #name" :" text" ," _" :" Root Mean Square Error PCC" },{" #name" :" keyword" ," $" :{" id" :" k0235" }," $$" :[{" #name" :" text" ," _" :" Pearson Correlation Coefficient MDG" },{" #name" :" keyword" ," $" :{" id" :" k0245" }," $$" :[{" #name" :" text" ," _" :" Mean Decrease Gini |
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