Volume 13 Issue 5
Sep.  2022
Turn off MathJax
Article Contents
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
Funds:

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.
  • loading
  • [1]
    Abedi Gheshlaghi, H., Feizizadeh, B., Blaschke, T., Lakes, T., Tajbar, S., 2021. Forest fire susceptibility modeling using hybrid approaches.Trans GIS.25,311-333
    [2]
    Abraham, M. T., Satyam, R., Pradhan, B., et al., 2021. Factors Affecting Landslide Susceptibility Mapping:Assessing the Influence of Different Machine Learning Approaches, Sampling Strategies and Data Splitting. Land 10 (9), https://doi.org/https://doi.org/10.3390/land10090989. In this issue
    [3]
    Abuzied, S.M., Pradhan, B., 2021. Hydro-geomorphic assessment of erosion intensity and sediment yield initiated debris-flow hazards at Wadi Dahab Watershed, Egypt.Georisk Assess. Manag. Risk Eng. Syst. Geohazards.15,221-246
    [4]
    Ahmad, H., Ningsheng, C., Rahman, M., Islam, M.M., Pourghasemi, H.R., Hussain, S.F., Habumugisha, J.M., Liu, E., Zheng, H., Ni, H., Dewan, A., 2021. Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models.ISPRS Int. J. Geoinf.10, 315
    [5]
    Aksha, S.K., Resler, L.M., Juran, L., Carstensen Jr, L.W., 2020. A geospatial analysis of multi-hazard risk in Dharan, Nepal. Geomatics, Nat. Hazards Risk 11, 88-111
    [6]
    Ali, S., Haider, R., Abbas, W., Basharat, M., Reicherter, K., 2021. Empirical assessment of rockfall and debris flow risk along the Karakoram Highway, Pakistan. Nat. Hazards 106, 2437-2460
    [7]
    Allan, R.P., Hawkins, E., Bellouin, N., Collins, B., 2021. IPCC, 2021:Summary for Policymakers.
    [8]
    Atta-Ur-Rahman, Shaw, Rajib, 2015. Flood Risk and Reduction Approaches in Pakistan. Disaster Risk Reduction Approaches in Pakistan. Springer Nature, pp. 77-100. In this issue
    [9]
    Audebert, N., Le Saux, B., Lefèvre, S., 2019. Deep learning for classification of hyperspectral data:A comparative review. IEEE Geosci. Remote Sens. Mag. 7, 159-173
    [10]
    Avand, M., Janizadeh, S., Naghibi, S.A., Pourghasemi, H.R., Khosrobeigi Bozchaloei, S., Blaschke, T., 2019. A comparative assessment of random forest and k-nearest neighbor classifiers for gully erosion susceptibility mapping. Water 11, 2076
    [11]
    Azarafza, M., Azarafza, M., Akgün, H., Atkinson, P.M., Derakhshani, R., 2021. Deep learning-based landslide susceptibility mapping.Sci. Rep.11,1-16
    [12]
    Azareh, A., Rafiei Sardooi, E., Choubin, B., Barkhori, S., Shahdadi, A., Adamowski, J., Shamshirband, S., 2021. Incorporating multi-criteria decision-making and fuzzy-value functions for flood susceptibility assessment. Geocarto Int 36, 2345-2365
    [13]
    Bathrellos, G.D., Skilodimou, H.D., Chousianitis, K., Youssef, A.M., Pradhan, B., 2017. Suitability estimation for urban development using multi-hazard assessment map. Sci. Total Environ. 575, 119-134
    [14]
    Bronkhorst, V.B., 2012. Disaster risk management in South Asia:regional overview. The World Bank
    [15]
    Bui, D.T., Hoang, N.-D., Martínez-Álvarez, F., Ngo, P.-T.T., Hoa, P.V., Pham, T.D., Samui, P., Costache, R., 2020. A novel deep learning neural network approach for predicting flash flood susceptibility:A case study at a high frequency tropical storm area. Sci. Total Environ. 701, 134413
    [16]
    Calle, M.L., Urrea, V., 2011. Letter to the editor:stability of random forest importance measures. Brief. Bioinform. 12, 86-89
    [17]
    Canziani, A., Paszke, A., Culurciello, E., 2016. An analysis of deep neural network models for practical applications. arXiv Prepr. arXiv1605.07678.
    [18]
    Cao, J., Zhang, Z., Du, J., Zhang, L., Song, Y., Sun, G., 2020. Multi-geohazards susceptibility mapping based on machine learning-a case study in Jiuzhaigou, China. Nat. Hazards 102, 851-871
    [19]
    Catani, F., Lagomarsino, D., Segoni, S., Tofani, V., 2013. Landslide susceptibility estimation by random forests technique:sensitivity and scaling issues. Nat. Hazards Earth Syst. Sci. 13, 2815-2831
    [20]
    Chen, J., Li, Y., Zhou, W., Iqbal, J., Cui, Z., 2017. Debris-flow susceptibility assessment model and its application in semiarid mountainous areas of the Southeastern Tibetan Plateau.Nat Hazards Rev.1, 05016005
    [21]
    Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y., 2014. Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. earth Obs. Remote Sens. 7, 2094-2107
    [22]
    Chen, Y., Qin, S., Qiao, S., Dou, Q., Che, W., Su, G., Yao, J., Nnanwuba, U.E., 2020. Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China. Water 12, 2079
    [23]
    Choi, K., Fazekas, G., Sandler, M., Cho, K., 2017. Convolutional recurrent neural networks for music classification, in:2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 2392-2396
    [24]
    Choubin, B., Borji, M., Mosavi, A., Sajedi-Hosseini, F., Singh, V.P., Shamshirband, S., 2019. Snow avalanche hazard prediction using machine learning methods.J. Hydrol. 577, 123929
    [25]
    Costache, R., Hong, H., Wang, Y., 2019. Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics. Catena. 183, 104179
    [26]
    Costache, R., Popa, M.C., Bui, D.T., Diaconu, D.C., Ciubotaru, N., Minea, G., Pham, Q.B., 2020. Costache, R., Popa, M.C., Bui, D.T., Diaconu, D.C., Ciubotaru, N., Minea, G. and Pham, Q.B., 2020. Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning. J. Hydrol. 585, 124808.
    [27]
    Chousianitis, K., Del Gaudio, V., Sabatakakis, N., et al., 2016. Assessment of earthquake-induced landslide hazard in Greece:From Arias intensity to spatial distribution of slope resistance demand. Bulletin of the Seismological Society of America 106 (1), 174-188, https://doi.org/https://doi.org/10.1785/0120150172. In this issue
    [28]
    Corominas, J., van Westen, C., Frattini, P., Cascini, L., Malet, J.P., Fotopoulou, S., Catani, F., Van Den Eeckhaut, M., Mavrouli, O., Agliardi, F., Pitilakis, K., 2014. Recommendations for the quantitative analysis of landslide risk.Bull. Eng. Geol. Environ.73, 209-263
    [29]
    Das, S., 2019. Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens. Appl. Soc. Environ. 14, 60-74
    [30]
    Dikshit, A., Pradhan, B., 2021. Interpretable and explainable AI (XAI) model for spatial drought prediction.Sci. Total Environ.801, 149797
    [31]
    Dikshit, A., Pradhan, B., Alamri, A.M., 2021. Pathways and challenges of the application of artificial intelligence to geohazards modelling.Gondwana Res.100, 290-301
    [32]
    Dou, J., Yunus, A.P., Merghadi, A., Shirzadi, A., Nguyen, H., Hussain, Y., Avtar, R., Chen, Y., Pham, B.T., Yamagishi, H., 2020. Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning.Sci. Total Environ. 720, 137320
    [33]
    Downton, M.W., Miller, J.Z.B., Pielke Jr, R.A., 2005. Reanalysis of US National Weather Service flood loss database. Nat. Hazards Rev. 6, 13-22
    [34]
    Eckstein, D., Künzel, V., Schäfer, L., 2021. Global Climate Risk Index 2021. Who Suff. Most from Extrem. Weather Events 2000-2019.
    [35]
    Fang, Z., Wang, Y., Peng, L., Hong, H., 2021. A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int. J. Geogr. Inf. Sci. 35, 321-347
    [36]
    Fang, Z., Wang, Y., Peng, L., Hong, H., 2020. Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping. Comput. Geosci. 139, 104470
    [37]
    Feizizadeh, B., Omarzadeh, D., Mohammadnejad, V., Khallaghi, H., Sharifi, A., Karkarg, B.G., 2022. An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran.J. Environ. Plan. Manag.1-23
    [38]
    Furlan, E., Torresan, S., Critto, A., Marcomini, A., 2018. Spatially explicit risk approach for multi-hazard assessment and management in marine environment:The case study of the Adriatic Sea. Sci. Total Environ. 618, 1008-1023
    [39]
    Ghosh, A., Maiti, R., 2021. Soil erosion susceptibility assessment using logistic regression, decision tree and random forest:study on the Mayurakshi river basin of Eastern India.Environ. Earth Sci.80,1-16
    [40]
    Goyes-Peñafiel, P., Hernandez-Rojas, A., 2021. Landslide susceptibility index based on the integration of logistic regression and weights of evidence:A case study in Popayan, Colombia.Eng Geol.280, 105958
    [41]
    Graham, O., Edwards, S., Robertson, R., 2022. Managing stakeholder relationships for improved situation awareness during volcanic emergencies:An Eastern Caribbean case study.Int. J. Disaster Risk Reduct.67, 102656
    [42]
    Habumugisha, J.M., Chen, N., Rahman, M., Islam, M.M., Ahmad, H., Elbeltagi, A., Sharma, G., Liza, S.N., Dewan, A., 2022. Landslide susceptibility mapping with deep learning algorithms.Sustainability.14,1734
    [43]
    Hosseini, F.S., Sigaroodi, S.K., Salajegheh, A., Moghaddamnia, A., Choubin, B., 2021. Towards a flood vulnerability assessment of watershed using integration of decision-making trial and evaluation laboratory, analytical network process, and fuzzy theories.Environ. Sci. Pollut. Res.28, 62487-62498
    [44]
    Hou, J., Zhou, N., Chen, G., Huang, M., Bai, G., 2021. Rapid forecasting of urban flood inundation using multiple machine learning models.Na Hazards.108,2335-2356
    [45]
    Huang, F., Cao, Z., Guo, J., Jiang, S.-H., Li, S., Guo, Z., 2020. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena 191, 104580
    [46]
    Hussain, M., Tayyab, M., Zhang, J., Shah, A.A., Ullah, K., Mehmood, U., Al-Shaibah, B., 2021. GIS-Based Multi-Criteria Approach for Flood Vulnerability Assessment and Mapping in District Shangla:Khyber Pakhtunkhwa, Pakistan. Sustainability 13, 3126
    [47]
    Javidan, N., Kavian, A., Pourghasemi, H.R., Conoscenti, C., Jafarian, Z., Rodrigo-Comino, J., 2021. Evaluation of multi-hazard map produced using MaxEnt machine learning technique.Sci. Rep.11,1-20
    [48]
    Jiang, P., Chen, J., 2016. Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing 198, 40-47
    [49]
    Kalantar, B., Ueda, N., Lay, U.S., Al-Najjar, H.A.H., Halin, A.A., 2019. Conditioning factors determination for landslide susceptibility mapping using support vector machine learning, in:IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp. 9626-9629
    [50]
    Kalantar, B., Ueda, N., Saeidi, V., Ahmadi, K., Halin, A.A., Shabani, F., 2020. Landslide susceptibility mapping:Machine and ensemble learning based on remote sensing big data. Remote Sens. 12, 1737
    [51]
    Kappes, M.S., Keiler, M., Glade, T., 2010. From single-to multi-hazard risk analyses:a concept addressing emerging challenges.
    [52]
    Kappes, M.S., Keiler, M., von Elverfeldt, K., Glade, T., 2012. Challenges of analyzing multi-hazard risk:a review.Nat hazards.64,1925-1958
    [53]
    Karpouza, M., Chousianitis, K., Bathrellos, G.D., Skilodimou, H.D., Kaviris, G., Antonarakou, A., 2021. Hazard zonation mapping of earthquake-induced secondary effects using spatial multi-criteria analysis.Nat Hazards.109, 637-669
    [54]
    Khan, Imran, Lei, H., Shah, A.A., Khan, Inayat, Muhammad, I., 2021. Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future. Environ. Sci. Pollut. Res. 1-12
    [55]
    Khosravi, K., Pourghasemi, H.R., Chapi, K., Bahri, M., 2016. Flash flood susceptibility analysis and its mapping using different bivariate models in Iran:a comparison between Shannon's entropy, statistical index, and weighting factor models. Environ. Monit. Assess. 188, 1-21
    [56]
    Khosravi, K., Shahabi, H., Pham, B.T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G., Ho, H.L., 2019. A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J. Hydrol. 573, 311-323
    [57]
    Kritikos, T., Robinson, T.R., Davies, T.R.H., 2015. Regional coseismic landslide hazard assessment without historical landslide inventories:A new approach. J. Geophys. Res. Earth Surf. 120, 711-729
    [58]
    LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521, 436-444
    [59]
    Li, Y., Chen, W., Rezaie, F., Rahmati, O., Davoudi Moghaddam, D., Tiefenbacher, J., Panahi, M., Lee, M.J., Kulakowski, D., Tien Bui, D., Lee, S., 2021. Debris flows modeling using geo-environmental factors:developing hybridized deep-learning algorithms.Geocarto Int.1-25
    [60]
    Liu, Mei, Zhang, Yong, Shufeng, Tian, et al., 2020. Effects of loose deposits on debris flow processes in the Aizi Valley, southwest China. Journal of Mountain Science 17, 156-172, https://doi.org/https://doi.org/10.1007/s11629-019-5388-9. In this issue
    [61]
    Lombardo, L., Tanyas, H., Nicu, I.C., 2020. Spatial modeling of multi-hazard threat to cultural heritage sites. Eng. Geol. 277, 105776
    [62]
    Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., Johnson, B.A., 2019. Deep learning in remote sensing applications:A meta-analysis and review. ISPRS J. Photogramm. Remote Sens. 152, 166-177
    [63]
    Mafi-Gholami, D., Zenner, E.K., Jaafari, A., Bakhtyari, H.R.R., Bui, D.T., 2019. Multi-hazards vulnerability assessment of southern coasts of Iran. J. Environ. Manage. 252, 109628
    [64]
    Mallat, S., 2016. Understanding deep convolutional networks. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150203
    [65]
    Mandal, K., Saha, S., Mandal, S., 2021. Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India. Geosci. Front. 12, 101203
    [66]
    Marin, G., Modica, M., Paleari, S., Zoboli, R., 2021. Assessing disaster risk by integrating natural and socio-economic dimensions:A decision-support tool.Socio-Econ. Plan. Sci.77, 101032
    [67]
    Marra, F., Destro, E., Nikolopoulos, E.I., Zoccatelli, D., Creutin, J.D., Guzzetti, F., Borga, M., 2017. Impact of rainfall spatial aggregation on the identification of debris flow occurrence thresholds. Hydrol. Earth Syst. Sci. 21, 4525-4532
    [68]
    Merghadi, A., Yunus, A.P., Dou, J., Whiteley, J., ThaiPham, B., Bui, D.T., Avtar, R., Abderrahmane, B., 2020. Machine learning methods for landslide susceptibility studies:A comparative overview of algorithm performance. Earth-Science Rev. 207, 103225
    [69]
    Mosavi, A., Golshan, M., Janizadeh, S., Choubin, B., Melesse, A.M., Dineva, A.A., 2020. Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping:a priority assessment of sub-basins.Geocarto Int.1-20
    [70]
    Nachappa, T.G., Ghorbanzadeh, O., Gholamnia, K., Blaschke, T., 2020. Multi-hazard exposure mapping using machine learning for the State of Salzburg, Austria. Remote Sens. 12, 2757
    [71]
    Nicodemus, K.K., 2011. Letter to the editor:On the stability and ranking of predictors from random forest variable importance measures. Brief. Bioinform. 12, 369-373
    [72]
    Oh, H.J., Pradhan, B., 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area.Comput Geosci.37, 1264-1276
    [73]
    Pham, V.D., Nguyen, Q.-H., Nguyen, H.-D., Pham, V.-M., Bui, Q.-T., 2020. Convolutional neural network-optimized moth flame algorithm for shallow landslide susceptible analysis. IEEE Access 8, 32727-32736
    [74]
    Pourghasemi, H.R., Gayen, A., Edalat, M., Zarafshar, M., Tiefenbacher, J.P., 2020. Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? Geosci. Front. 11, 1203-1217
    [75]
    Pourghasemi, H.R., Gayen, A., Panahi, M., Rezaie, F., Blaschke, T., 2019. Multi-hazard probability assessment and mapping in Iran. Sci. Total Environ. 692, 556-571
    [76]
    Pourghasemi, H.R., Kerle, N., 2016. Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ. earth Sci. 75, 185
    [77]
    Pouyan, S., Pourghasemi, H.R., Bordbar, M., Rahmanian, S., Clague, J.J., 2021. A multi-hazard map-based flooding, gully erosion, forest fires, and earthquakes in Iran. Sci. Rep. 11, 1-19
    [78]
    Rafiei-Sardooi, E., Azareh, A., Choubin, B., Mosavi, A.H., Clague, J.J., 2021. Evaluating urban flood risk using hybrid method of TOPSIS and machine learning.Int. J. Disaster Risk Reduct.66,102614
    [79]
    Rahman, M., Chen, N., Elbeltagi, A., Islam, M.M., Alam, M., Pourghasemi, H.R., Tao, W., Zhang, J., Shufeng, T., Faiz, H., 2021. Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh. J. Environ. Manage. 295, 113086
    [80]
    Rahmati, O., Yousefi, S., Kalantari, Z., Uuemaa, E., Teimurian, T., Keesstra, S., Pham, T.D., Tien Bui, D., 2019. Multi-hazard exposure mapping using machine learning techniques:A case study from Iran. Remote Sens. 11, 1943
    [81]
    Ranjbar, S., Hooshyar, M., Singh, A., Wang, D., 2018. Quantifying climatic controls on river network branching structure across scales. Water Resour. Res. 54, 7347-7360
    [82]
    Sanam, K. Aksha, Lynn, M. Resler, Luke, Juran, et al., 2020. A geospatial analysis of multi-hazard risk in Dharan, Nepal. Geomatics, Natural Hazards and Risk 11 (1), 88-111, https://doi.org/https://doi.org/10.1080/19475705.2019.1710580. In this issue
    [83]
    Segoni, S., Caleca, F., 2021. Definition of Environmental Indicators for a Fast Estimation of Landslide Risk at National Scale.Land.10, 621
    [84]
    Roy, J., Saha, S., 2019. Landslide susceptibility mapping using knowledge driven statistical models in Darjeeling District, West Bengal, India. Geoenvironmental Disasters 6, 1-18
    [85]
    Shaw, R., 2015. Disaster and climate change education in Pakistan, in:Disaster Risk Reduction Approaches in Pakistan. Springer, pp. 315-335.
    [86]
    Simard, P.Y., Steinkraus, D., Platt, J.C., 2003. Best practices for convolutional neural networks applied to visual document analysis., in:Icdar
    [87]
    Simonyan, K., Zisserman, A., 2014. Very deep convolutional networks for large-scale image recognition. arXiv Prepr. arXiv1409.1556.
    [88]
    Skilodimou, H.D., Bathrellos, G.D., Chousianitis, K., Youssef, A.M., Pradhan, B., 2019. Multi-hazard assessment modeling via multi-criteria analysis and GIS:a case study. Environ. Earth Sci. 78, 47
    [89]
    Song, J., Wang, Y., Fang, Z., Peng, L., Hong, H., 2020. Potential of Ensemble Learning to Improve Tree-Based Classifiers for Landslide Susceptibility Mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 4642-4662
    [90]
    Sun, Z., Sandoval, L., Crystal-Ornelas, R., Mousavi, S.M., Wang, J., Lin, C., Cristea, N., Tong, D., Carande, W.H., Ma, X. and Rao, Y., 2022. A review of Earth Artificial Intelligence.Comput Geosci. 105034
    [91]
    Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions, in:Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1-9
    [92]
    Tehrany, M.S., Pradhan, B., Jebur, M.N., 2015. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch. Environ. Res. risk Assess. 29, 1149-1165
    [93]
    Uitto, J.I., Shaw, R., 2016. Sustainable development and disaster risk reduction:Introduction, in:Sustainable Development and Disaster Risk Reduction. Springer, pp. 1-12.
    [94]
    Ullah, F., Saqib, S.E., Ahmad, M.M., Fadlallah, M.A., 2020. Flood risk perception and its determinants among rural households in two communities in Khyber Pakhtunkhwa, Pakistan. Nat. Hazards 104, 225-247
    [95]
    Ullah, K., Zhang, J., 2020. GIS-based flood hazard mapping using relative frequency ratio method:A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. PLoS One 15, e0229153
    [96]
    UN, 2002. Johannesburg Plan of Implementation of the World Summit on Sustainable Development, Technical Report. United Nations
    [97]
    UNEP, 1992. Agenda 21.Tech. rep., United Nations Environment Programme. http://www. un.org/esa/dsd/agenda21/res_agenda21_07.shtml, Accessed on:3 September 2009.
    [98]
    Wang, Y., Fang, Z., Hong, H., 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci. Total Environ. 666, 975-993
    [99]
    Wang, Y., Fang, Z., Hong, H., Peng, L., 2020a. Flood susceptibility mapping using convolutional neural network frameworks. J. Hydrol. 582, 124482
    [100]
    Wang, Y., Fang, Z., Wang, M., Peng, L., Hong, H., 2020b. Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comput. Geosci. 138, 104445
    [101]
    Wasson, R.J., 1978. A debris flow at Reshūn, Pakistan Hindu Kush. Geogr. Ann. Ser. A, Phys. Geogr. 60, 151-159
    [102]
    Wu, S., Chen, J., Zhou, W., Iqbal, J., Yao, L., 2019. A modified Logit model for assessment and validation of debris-flow susceptibility. Bull. Eng. Geol. Environ. 78, 4421-4438
    [103]
    Yamashita, R., Nishio, M., Do, R.K.G., Togashi, K., 2018. Convolutional neural networks:an overview and application in radiology. Insights Imaging 9, 611-629
    [104]
    Yanar, T., Kocaman, S., Gokceoglu, C., 2020. Use of Mamdani fuzzy algorithm for multi-hazard susceptibility assessment in a developing urban settlement (Mamak, Ankara, Turkey). ISPRS Int. J. Geo-Information 9, 114
    [105]
    Yariyan, P., Omidvar, E., Minaei, F., Ali Abbaspour, R., Tiefenbacher, J.P., 2022. An optimization on machine learning algorithms for mapping snow avalanche susceptibility.Nat Hazards. 111,79-114
    [106]
    Yi, Y., Zhang, Z., Zhang, W., Jia, H., Zhang, J., 2020. Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network:A case study in Jiuzhaigou region. Catena 195, 104851
    [107]
    Youssef, A.M., Pradhan, B., Dikshit, A., Al-Katheri, M.M., Matar, S.S., Mahdi, A.M., 2022. Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms:comparison of their performance at Asir Region, KSA.Bull. Eng. Geol. Environ,81, 1-22
    [108]
    Youssef, A.M., Pradhan, B., Dikshit, A., Mahdi, A.M., 2022. Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping:a case study at Ras Gharib, Red Sea. Geocarto Int.1-26
    [109]
    Yousefi, S., Pourghasemi, H.R., Emami, S.N., Pouyan, S., Eskandari, S., Tiefenbacher, J.P., 2020. A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci. Rep. 10, 1-14
    [110]
    Zêzere, J.L., Pereira, S., Melo, R., et al., 2017. Mapping landslide susceptibility using data-driven methods. Sci. Total Environ. 589 (1), 250-267, https://doi.org/https://doi.org/10.1016/j.scitotenv.2017.02.188. In this issue
    [111]
    Zhang, G., Wang, M., Liu, K., 2019. Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China. Int. J. Disaster Risk Sci. 10, 386-403
    [112]
    Zhao, G., Pang, B., Xu, Z., Peng, D., Zuo, D., 2020. Urban flood susceptibility assessment based on convolutional neural networks. J. Hydrol. 590, 125235
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (40) PDF downloads(6) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return