Toward the development of deep learning analyses for snow avalanche releases in mountain regions

References

• Akay H. 2021. Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques. Catena. 206:105524.
   Web of Science ®Google Scholar
• Amin J, Sharif M, Yasmin M, Fernandes SL. 2018. Big data analysis for brain tumor detection: deep convolutional neural networks. Future Gener Comput Syst. 87:290–297.
   Web of Science ®Google Scholar
• Ancey C. 2009. Snow avalanches. In: Delage P, Schrefler B, editors. New York: Wiley & Sons.
   Google Scholar
• Aydin A, Eker R. 2017. GIS-Based snow avalanche hazard mapping: Bayburt-Aşağı Dere catchment case. JEB. 38(5(SI)):937–943.
   Google Scholar
• Babu SB, Suneetha A, Babu GC, Kumar YJN, Karuna G. 2018. Medical disease prediction using grey wolf optimization and auto encoder based recurrent neural network. PEN. 6(1):229–240.
   Google Scholar
• Barbolini M, Pagliardi M, Ferro F, Corradeghini P. 2011. Avalanche hazard mapping over large undocumented areas. Nat Hazards. 56(2):451–464.
   Web of Science ®Google Scholar
• Barros AK, Carlos Príncipe J, Erdogmus D. 2007. Independent component analysis and blind source separation. Signal Process. 87(8):1817–1818.
   Web of Science ®Google Scholar
• Barros AK, Cichocki A. 2001. Extraction of specific signals with temporal structure. Neural Comput. 13(9):1995–2003.
   PubMed Web of Science ®Google Scholar
• Bebi P, Kulakowski D, Rixen C. 2009. Snow avalanche disturbances in forest ecosystems. State of research and implications for management. For Ecol Manag. 257(9):1883–1892.
   Web of Science ®Google Scholar
• Benedikt J. 2002. Risk assessment of avalanches. A fuzzy GIS application. Proc 5th Int FLINS Conf. 2002:395–402.
   Google Scholar
• Beven K, Kirkby M. 1979. A physically based, variable contributing area model of basin hydrology/Un modèle à base physique de zone d’appel variable de l’hydrologie du bassin versant. Hydrol Sci J. 24(1):43–69.
   Google Scholar
• Bocchiola D, Medagliani M, Rosso R. 2006. Regional snow depth frequency curves for avalanche hazard mapping in central Italian Alps. Cold Reg Sci Technol. 46(3):204–221.
   Web of Science ®Google Scholar
• Bochinski E, Senst T, Sikora T. 2017. Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. In: 2017 IEEE International Conference on Image Processing (ICIP). Beijing: IEEE; p. 3924–3928.
   Google Scholar
• Bourova E, Maldonado E, Leroy JB, Alouani R, Eckert N, Bonnefoy-Demongeot M, Deschatres M. 2016. A new web-based system to improve the monitoring of snow avalanche hazard in France. Nat Hazards Earth Syst Sci. 16(5):1205–1216.
   Web of Science ®Google Scholar
• Bühler Y, Hüni A, Christen M, Meister R, Kellenberger T. 2009. Automated detection and mapping of avalanche deposits using airborne optical remote sensing data. Cold Reg Sci Technol. 57(2–3):99–106.
   Web of Science ®Google Scholar
• Butler DR, Walsh SJ. 1990. Lithologic, structural, and topographic influences on snow‐avalanche path location, Eastern Glacier National Park, Montana. Ann Assoc Am Geographers. 80(3):362–378.
   Web of Science ®Google Scholar
• Can R, Kocaman S, Gokceoglu C. 2019. A convolutional neural network architecture for auto-detection of landslide photographs to assess citizen science and volunteered geographic information data quality. IJGI. 8(7):300.
   Google Scholar
• Canziani A, Paszke A, Culurciello E. 2016. An analysis of deep neural network models for practical applications. arXiv. preprint arXiv:1605. 07678 [cs.CV].
   Google Scholar
• Cappabianca F, Barbolini M, Natale L. 2008. Snow avalanche risk assessment and mapping: a new method based on a combination of statistical analysis, avalanche dynamics simulation and empirically-based vulnerability relations integrated in a GIS platform. Cold Reg Sci Technol. 54(3):193–205.
   Web of Science ®Google Scholar
• Chen X, Chen W. 2021. GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena. 196:104833.
   Web of Science ®Google Scholar
• Chen W, Chen X, Peng J, Panahi M, Lee S. 2021. Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosci Front. 12(1):93–107.
   Web of Science ®Google Scholar
• Chen W, Chen Y, Tsangaratos P, Ilia I, Wang X. 2020. Combining evolutionary algorithms and machine learning models in landslide susceptibility assessments. Remote Sens. 12(23):3854.
   Google Scholar
• Chen W, Hong H, Panahi M, Shahabi H, Wang Y, Shirzadi A, Pirasteh S, Alesheikh AA, Khosravi K, Panahi S, et al. 2019. Spatial prediction of landslide susceptibility using gis-based data mining techniques of ANFIS with whale optimization algorithm (WOA) and grey wolf optimizer (GWO). Appl Sci. 9(18):3755.
   Google Scholar
• Chen Y, Jiang H, Li C, Jia X, Ghamisi P. 2016. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Trans Geosci Remote Sensing. 54(10):6232–6251.
   Web of Science ®Google Scholar
• Chen W, Li Y. 2020. GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena. 195:104777.
   Web of Science ®Google Scholar
• Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Wang X, Ahmad BB. 2020. Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol. 583:124602.
   Web of Science ®Google Scholar
• Chlögl M, Matulla C. 2018. Potential future exposure of European land transport infrastructure to rainfall-inducedlandslides throughout the 21st century. Nat Hazards Earth Syst Sci. 18(4):1121–1132.
   Web of Science ®Google Scholar
• Choubin B, Borji M, Mosavi A, Sajedi-Hosseini F, Singh VP, Shamshirband S. 2019. Snow avalanche hazard prediction using machine learning methods. J Hydrol. 577:123929.
   Web of Science ®Google Scholar
• Christophe C, Georges R, Jérôme LS, Markus S, Pascal P. 2010. Spatio-temporal reconstruction of snow avalanche activity using tree rings: Pierres Jean Jeanne avalanche talus, Massif de l'Oisans, France. Catena. 83(2–3):107–118.
   Web of Science ®Google Scholar
• Comon P. 1994. Independent component analysis. A new concept? Signal Process. 36(3):287–314.
   Web of Science ®Google Scholar
• Conrad O, Bechtel B, Bock M, Dietrich H, Fischer E, Gerlitz L, Wehberg J, Wichmann V, Böhner J. 2015. System for automated geoscientific analyses (SAGA) v. 2.1. 4. Geosci Model Dev. 8(7):1991–2007.
   Web of Science ®Google Scholar
• Costa MH, Botta A, Cardille JA. 2003. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Amazonia. J Hydrol. 283(1–4):206–217.
   Web of Science ®Google Scholar
• Decaulne A, Saemundsson T. 2006. Geomorphic evidence for present-day snow-avalanche and debris-flow impact in the Icelandic Westfjords. Geomorphology. 80(1–2):80–93.
   Web of Science ®Google Scholar
• Douglas-Smith D, Iwanaga T, Croke BFW, Jakeman AJ. 2020. Certain trends in uncertainty and sensitivity analysis: an overview of software tools and techniques. Environ Model Software. 124:104588.
   Web of Science ®Google Scholar
• Elfert S, Bormann H. 2010. Simulated impact of past and possible future land use changes on the hydrological response of the Northern German lowland “Hunte” catchment. J Hydrol. 383(3–4):245–255.
   Web of Science ®Google Scholar
• Falah F, Nejad S G, Rahmati O, Daneshfar M, Zeinivand H. 2016. Applicability of generalized additive model in groundwater potential modelling and comparison its performance by bivariate statistical methods. Geocarto Int. 31(1):1–21.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Fawcett T. 2006. An introduction to ROC analysis. Pattern Recogn Lett. 27(8):861–874.
   Web of Science ®Google Scholar
• Feddema JJ, Oleson KW, Bonan GB, Mearns LO, Buja LE, Meehl GA, Washington WM. 2005. The importance of land-cover change in simulating future climates. Science. 310(5754):1674–1678.
   PubMed Web of Science ®Google Scholar
• Fuchs S, Bründl M. 2005. Damage potential and losses resulting from snow avalanches in settlements of the Canton of Grisons, Switzerland. Nat Hazards. 34(1):53–69.
   Web of Science ®Google Scholar
• Gasparini NM, Tucker GE, Bras RL. 2004. Network‐scale dynamics of grain‐size sorting: implications for downstream fining, stream‐profile concavity, and drainage basin morphology. Earth Surf Process Landforms. 29(4):401–421.
   Web of Science ®Google Scholar
• Gebrehiwot A, Hashemi-Beni L, Thompson G, Kordjamshidi P, Langan TE. 2019. Deep convolutional neural network for flood extent mapping using unmanned aerial vehicles data. Sensors. 19(7):1486.
   PubMed Web of Science ®Google Scholar
• Ghimire B, Rogan J, Rodríguez-Galiano V, Panday P, Neeti N. 2012. An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GISci Remote Sens. 49(5):623–643.
   Web of Science ®Google Scholar
• Gleason J. 1994. Review-terrain parameters of avalanche starting zones and their effect on avalanche frequency. In International Snow Science Workshop, Snowbird, Utah.
   Google Scholar
• Hu F, Xia GS, Hu J, Zhang L. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens. 7(11):14680–14707.
   Google Scholar
• Hyvrinen A. 2010. Independent component analysis. New York (NY): Wiley & Sons.
   Google Scholar
• Jad SMM, Geravandi S, Mohammadi MJ, Alizadeh R, Sarvarian M, Rastegarimehr B, Afkar A, Yari AR, Momtazan M, Valipour A, et al. 2017. The relationship between knowledge of leadership and knowledge management practices in the food industry in Kurdistan province, Iran. Data Brief. 15:155–159.
   PubMed Web of Science ®Google Scholar
• Jamieson B, Stethem C. 2002. Snow avalanche hazards and management in Canada: challenges and progress. Natural Hazards. 26(1):35–53.
   Web of Science ®Google Scholar
• Ji S, Yu D, Shen C, Li W, Xu Q. 2020. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides. 17(6):1316–1337.
   Web of Science ®Google Scholar
• Kavzoglu T, Sahin EK, Colkesen I. 2014. Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides. 11(3):425–439.
   Web of Science ®Google Scholar
• Knudby A, LeDrew E, Brenning A. 2010. Predictive mapping of reef fish speciesrichness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques. Remote Sens Environ. 114(6):1230–1241.
   Web of Science ®Google Scholar
• Krizhevsky A, Sutskever I, Hinton GE. 2012. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 1:1097–1105. Available from: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neuralnetworks.pdf.
   Google Scholar
• Kulakowski D, Rixen C, Bebi P. 2006. Changes in forest structure and in the relative importance of climatic stress as a result of suppression of avalanche disturbances. For Ecol Manage. 223(1–3):66–74.
   Web of Science ®Google Scholar
• Kumar S, Srivastava PK. 2018. Geospatial modelling and mapping of snow avalanche susceptibility. J Ind Soc Remote Sens. 46(1):109–119.
   Web of Science ®Google Scholar
• Kumar S, Srivastava PK, Snehmani. 2017. GIS-based MCDA–AHP modelling for avalanche susceptibility mapping of Nubra valley region, Indian Himalaya. Geocarto Int. 32(11):1254–1267.
   Web of Science ®Google Scholar
• Kumar S, Snehmani, Srivastava PK, Gore A, Singh MK. 2016. Fuzzy–frequency ratio model for avalanche susceptibility mapping. Int J Digit Earth. 9(12):1168–1184.
   Web of Science ®Google Scholar
• Laamrani A, Valeria O, Bergeron Y, Fenton N, Cheng LZ. 2015. Distinguishing and mapping permanent and reversible paludified landscapes in Canadian black spruce forests. Geoderma. 237–238:88–97.
   Web of Science ®Google Scholar
• Lecun Y, Bengio Y, Hinton G. 2015. Deep learning. Nature. 521(7553):436–444.
   PubMed Web of Science ®Google Scholar
• Li Y, Martinis S, Wieland M. 2019. Urban flood mapping with an active self-learning convolutional neural network based on TerraSAR-X intensity and interferometric coherence. ISPRS J Photogramm Remote Sens. 152:178–191.
   Web of Science ®Google Scholar
• Li Q, Pham HA. 2017. A testing-coverage software reliability model considering fault removal efficiency and error generation. PloS One. 12(7):e0181524.
   PubMed Web of Science ®Google Scholar
• Lim MK, Tang S, Chan CS. 2014. iSurveillance: intelligent framework for multiple events detection in surveillance videos. Expert Syst Appl. 41(10):4704–4715.
   Web of Science ®Google Scholar
• Maggioni M. 2005. Avalanche release areas and their influence on uncertainty in avalanche hazard mapping [PhD thesis]. Universität Zürich; p. 146.
   Google Scholar
• Mainieri R, Favillier A, Lopez-Saez J, Eckert N, Zgheib T, Morel P, Saulnier M, Peiry JL, Stoffel M, Corona C. 2020. Impacts of land-cover changes on snow avalanche activity in the French Alps. Anthropocene. 30:100244.
   Web of Science ®Google Scholar
• Mao D, Cherkauer KA. 2009. Impacts of land-use change on hydrologic responses in the Great Lakes region. J. Hydrol. 374(1-2):71–82.
   Web of Science ®Google Scholar
• Marriott FHC. 1990. Adictionary of statistical terms, 5th ed. New York: Wiley.
   Google Scholar
• Mason SJ, Graham NE. 2002. Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Q J R Meteorol Soc. 128(584):2145–2166.
   Web of Science ®Google Scholar
• Maxion RA, Roberts RR. 2004. Proper use of ROC curves in intrusion/anomaly detection, technical report series CS-TR-871. Published by the University of Newcastle upon Tyne, School of Computing Science, Newcastle upon Tyne, UK; p. 32.
   Google Scholar
• McClung D, Schaerer P. 2006. The avalanche handbook, 3rd ed. Seattle (WA): The Mountaineers Books.
   Google Scholar
• Mirjalili S, Mirjalili SM, Lewis A. 2014. Grey wolf optimizer. Adv Eng Softw. 69:46–61.
   Web of Science ®Google Scholar
• Oh HJ, Kim YS, Choi JK, Park E, Lee S. 2011. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol. 399(3–4):158–172.
   Web of Science ®Google Scholar
• Oquab M, Bottou L, Laptev I, Sivic J. 2014. Learning and transferring mid-level image representations using convolutional neural networks. In Conference on Computer Vision and Pattern Recognition (CVPR). Columbus (OH): IEEE; p. 1717–1724.
   Google Scholar
• Parshad R, Kumar P, Srivastva PK. 2019. Seismically induced snow avalanches at Nubra–Shyok region of Western Himalaya, India. Natural Hazards. 99(2):843–855.
   Web of Science ®Google Scholar
• Peitzsch EH, Hendrikx J, Fagre DB. 2015. Terrain parameters of glide snow avalanches and a simple spatial glide snow avalanche model. Cold Reg Sci Technol. 120:237–250.
   Web of Science ®Google Scholar
• Pourali SH, Arrowsmith C, Chrisman N, Matkan AA, Mitchell D. 2016. Topography wetness index application in flood-risk-based land use planning. Appl Spatial Anal. 9(1):39–54.
   Web of Science ®Google Scholar
• Puntonet CG, Lang EW. 2006. Blind source separation and independent component analysis. Neurocomputing. 69(13–15):1413.
   Web of Science ®Google Scholar
• Rahmati O, Ghorbanzadeh O, Teimurian T, Mohammadi F, Tiefenbacher JP, Falah F, Pirasteh S, Ngo PTT, Bui DT. 2019. Spatial modeling of snow avalanche using machine learning models and geo-environmental factors: comparison of effectiveness in two mountain regions. Remote Sens. 11(24):2995.
   Google Scholar
• Recknagel F, Bobbin J, Whigham P, Wilson H. 2000. Multivariate time-series modelling of algal blooms in freshwater lakes by machine learning. In: Vanrolleghem P, Lessard P, editors. Proceedings of the 5th International Symposium WATERMATEX’2000 on Systems Analysis and Computing in Water Quality Management. London: IWA Publishing.
   Google Scholar
• Ren S, He K, Girshick R, Sun J. 2015. Faster R-CNN: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst. 39(6):1137–1149.
   Google Scholar
• Rixen C, Haag S, Kulakowski D, Bebi P. 2007. Natural disturbance modulates plant diversity and species composition in subalpine forest. J Vegetat Sci. 18(5):735–742.
   Web of Science ®Google Scholar
• Robinson S. 2014. Simulation: the practice of model development and use. 2nd ed. New York: Palgrave Macmillan.
   Google Scholar
• Rogan J, Miller J, Stow D, Franklin J, Levien L, Fischer C. 2003. Land-cover change monitoring with classification trees using landsat TM and ancillary data. Photogramm Eng Remote Sens. 69(7):793–804.
   Web of Science ®Google Scholar
• Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, et al. 2015. ImageNet large scale visual recognition challenge. Int J Comput Vis. 115(3):211–252.
   Web of Science ®Google Scholar
• Sameen MI, Pradhan B, Lee S. 2020. Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. Catena. 186:104249.
   Web of Science ®Google Scholar
• Schweizer J, Bruce JJ, Schneebeli M. 2003. Snow avalanche formation. Rev Geophys. 41(4):RG00012.
   Web of Science ®Google Scholar
• Sklar LS, Dietrich WE. 2004. A mechanistic model for river incision into bedrock by saltating bed load. Water Resour Res. 40(6):WR002496.
   Web of Science ®Google Scholar
• Sørensen R, Zinko U, Seibert J. 2006. On the calculation of the topographic wetness index: evaluation of different methods based on field observations. Hydrol Earth Syst Sci. 10(1):101–112.
   Web of Science ®Google Scholar
• Stethem C, Jamieson B, Schaerer P, Liverman D, Germain D, Walker S. 2003. Snow avalanche hazard in Canada – a review. Natural Hazards. 28(2/3):487–515.
   Web of Science ®Google Scholar
• Suk P, Klimánek M. 2014. Creation of the snow avalanche susceptibility map of the Krkonoše Mountains using GIS. Acta Univ Agric Silvic Mendelianae Brun. 59(5):237–245.
   Google Scholar
• Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. 9. Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition. Piscataway (NJ): IEEE; p. 1–9.
   Google Scholar
• Thommeret N, Bailly JS, Puech C. 2010. Extraction of thalweg networks from DTM’s: application to badlands. Hydrol Earth Syst Sci. 14(8):1527–1536.
   Web of Science ®Google Scholar
• Tien Bui B, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, Khosravi k, Panahi M, Ahmad B, Saro L. 2018. Land subsidence susceptibility mapping in South using machine learning algorithms. Sensors. 18(8):2464.
   PubMed Web of Science ®Google Scholar
• Tu J. 2009. Combined impact of climate and land use changes on streamflow and water quality in eastern Massachusetts, USA. J Hydrol. 379(3–4):268–283.
   Web of Science ®Google Scholar
• Veitinger J, Sovilla B, Purves RS. 2014. Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach. Cryosphere. 8(2):547–569.
   Web of Science ®Google Scholar
• Vijith H, Dodge-Wan D. 2018. Spatio-temporal changes in rate of soil loss and erosion vulnerability of selected region in the tropical forests of Borneoduring last three decades. Earth Sci Inform. 11(2):171–181.
   Web of Science ®Google Scholar
• Voiculescu M, Popescu F. 2011. Management of snow avalanche risk in the ski areas of the southern Carpathians-Romanian Carpathians. Case study: The Balea (fagaras Massif) and Sinaia (Bucegi Mountains) ski areas. In: Zhelezov G, editor. Sustainable development in mountain regions: southern Europe. Dordrecht: Springer Netherlands; p. 103–121.
   Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Wang Y, Fang Z, Hong H, Peng L. 2020. Flood susceptibility mapping using convolutional neural network frameworks. J Hydrol. 582:124482.
   Web of Science ®Google Scholar
• Weimer D, Scholz-Reiter B, Shpitalni M. 2016. Design of deep convolutional neural network architectures for automated feature extraction in industrial inspection. CIRP Ann. 65(1):417–420.
   Web of Science ®Google Scholar
• Weiss A. 2001. Topographic positions and landforms analysis (conference poster). In ESRI International User Conference. San Diego, CA: The Nature Conservancy; p. 9–13.
   Google Scholar
• Wever N, Vera Valero C, Fierz C. 2016. Assessing wet snow avalanche activity using detailed physics based snowpack simulations. Geophys Res Lett. 43(11):5732–5740.
   Web of Science ®Google Scholar
• Yariyan P, Avand M, Abbaspour RA, Karami M, Tiefenbacher JP. 2020. GIS-based spatial modeling of snow avalanches using four novel ensemble models. Sci Total Environ. 745:141008.
   PubMed Web of Science ®Google Scholar
• Yesilnacar EK. 2005. The application of computational intelligence to landslide susceptibility mapping in Turkey [PhD Thesis]. Department of Geomatics, The University of Melbourne; p. 423.
   Google Scholar
• Yndman RJ, Koehler AB. 2006. Another look at measures of forecast accuracy. Int J Forecast. 22(4):679–688.
   Web of Science ®Google Scholar
• Yousefi S, Pourghasemi HR, Emami SN, Pouyan S, Eskandari S, Tiefenbacher JP. 2020. A machine learning framework for multi-hazards modeling and mapping in a mountainous area. Sci Rep. 10(1):1–14.
   PubMed Web of Science ®Google Scholar
• Yu X, Hu D. 2014. Blind source separation: Theory and applications. Singapore: John Wiley & Sons.
   Google Scholar
• Yu H, Ma Y, Wang L, Zhai Y, Wang X. 2017. A landslide intelligent detection method based on CNN and RSG_R. In 2017 IEEE International Conference on Mechatronics and Automation (ICMA). Takamatsu (Japan): IEEE; p. 40–44.
   Google Scholar
• Zhang P. 2019. A novel feature selection method based on global sensitivity analysis with application in machine learning-based prediction model. Appl Soft Comput. 85:105859.
   Web of Science ®Google Scholar
• Zhang E, Chen W, Zhang Z, Zhang Y. 2016. Local surface geometric feature for 3D human action recognition. J Neurocomput. 208(5):281–289.
   Google Scholar
• Zhang C, Pan X, Li H, Gardiner A, Sargent I, Hare J, Atkinson PM. 2018. A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS J Photogramm Remote Sens. 140:133–144.
   Web of Science ®Google Scholar
• Zhao X, Chen W. 2020. Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens. 12(14):2180.
   Google Scholar
• Zhao G, Pang B, Xu Z, Peng D, Zuo D. 2020. Urban flood susceptibility assessment based on convolutional neural networks. J Hydrol. 590:125235.
   Web of Science ®Google Scholar
• Žvokelj M, Zupan S, Prebil I. 2016. EEMD-based multiscale ICA method for slewing bearing fault detection anddiagnosis. J Sound Vib. 370:394–423.
   Web of Science ®Google Scholar