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重庆大学土木工程学院,重庆 400045
Received:16 October 2020,
Revised:2020-10-29,
Published:15 August 2021
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刘汉龙,马彦彬,仉文岗.大数据技术在地质灾害防治中的应用综述[J].防灾减灾工程学报,2021,41(04):710-722.
LIU Hanlong,MA Yanbin,ZHANG Wengang.Application of Big Data Techniques in Geological Disaster Analysis and Prevention: A Systematic Review[J].Journal of Disaster Prevention and Mitigation Engineering,2021,41(04):710-722.
刘汉龙,马彦彬,仉文岗.大数据技术在地质灾害防治中的应用综述[J].防灾减灾工程学报,2021,41(04):710-722. DOI: 10.13409/j.cnki.jdpme.2021.04.002.
LIU Hanlong,MA Yanbin,ZHANG Wengang.Application of Big Data Techniques in Geological Disaster Analysis and Prevention: A Systematic Review[J].Journal of Disaster Prevention and Mitigation Engineering,2021,41(04):710-722. DOI: 10.13409/j.cnki.jdpme.2021.04.002.
近年来,滑坡、崩塌、泥石流等地质灾害频发,其危害性大,波及范围广,严重威胁人民群众生命及财产安全,制约经济社会发展和人民对美好生活需求的向往。经过多年技术攻关和群防群测工作积累,我国在地质灾害风险调查和隐患排查方面取得了明显成效,综合运用合成孔径雷达测量、高分辨率卫星遥感、无人机遥感、机载激光雷达测量等多种新技术手段以提高全国地质灾害调查评价精度的工作也在持续开展中。在新时代计算机技术的不断革新与发展下,基于大数据技术的地质灾害监测预警为地质灾害防治提供了新的思维范式和经验指导。为了促进对该领域发展新导向的深入了解,介绍了大数据方法在地质灾害数据获取、存储、分析的几种关键技术,综述了迄今国内外学者利用大数据技术开展地质灾害研究和防治方面的工作。
In recent years, frequent geological disasters such as landslides, debris flows, and rock collapses, which are featured by severe harm and wide spread, threaten people’s lives and property safety, and restrict economic and social development and people’s desire for a better life. After years of technical research, mass prediction and disaster prevention, China has achieved remarkable success in geological disaster risk and hidden danger investigation. In order to improve the accuracy of national geological disaster survey and evaluation, comprehensive use of a series of new technical methods, such as synthetic aperture radar, high-resolution satellite remote sensing, unmanned aerial vehicle remote sensing and airborne lidar measurement, is underway. With the innovation and development of computer technology in the new era, the monitoring and early warning for geological disasters based on big data technology provide a new thinking paradigm and experience guidance for the prevention and control of geological disasters. For promoting an in-depth understanding of the new development direction in this field, key techniques in big data methods for acquisition, storage and analysis of geological disaster information are introduced, and progress on geological disasters analysis and prevention by worldwide scholars using big data techniques are reviewed.
殷跃平 . 中国地质灾害减灾回顾与展望——从国际减灾十年到国际减灾战略 [J]. 国土资源科技管理 , 2001 , 18 ( 3 ): 26 - 29 .
Yin Y P . A review and vision of geological hazards in China [J]. Scientific and Technological Management of Land and Resources , 2001 , 18 ( 3 ) : 26 - 29 .
Ghemawat S , Gobioff H , Leung S T . The Google file system [J]. Acm Sigops Operating Systems Review , 2003 : 37 : 29 - 43
Dean J , Ghemawat S . MapReduce: simplified data processing on large clusters [J]. Communications of the ACM , 2008 , 51 ( 1 ): 107 - 113 .
Chang F , Dean J , Ghemawat S , et al . Bigtable: A distributed storage system for structured data [J]. Acm Transactions on Computer Systems , 2008 , 26 ( 2 ): 1 - 26 .
Andreu-Perez J , Poon C C Y , Merrifield R D , et al . Big data for health [J]. IEEE Journal of Biomedical and Health Informatics , 2015 , 19 ( 4 ): 1193 - 1208 .
Lv Y S , Duan Y J , Kang W W , et al . Traffic flow prediction with big data: a deep learning approach [J]. IEEE Transactions on Intelligent Transportation Systems , 2015 , 16 ( 2 ): 865 - 873 .
Chen C L P , Zhang C Y . Data-intensive applications, challenges, techniques and technologies: A survey on big data [J]. Information Sciences , 2014 , 275 : 314 - 347 .
Lei Y , Jia F , Lin J , et al . An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data [J]. IEEE Transactions on Industrial Electronics , 2016 , 63 ( 5 ): 3137 - 3147 .
Kim G H , Trimi S , Chung J H . Big-data applications in the government sector [J]. Communications of the ACM , 2014 , 57 ( 3 ): 78 - 85 .
Fotovatikhah F , Herrera M , Shamshirband S , et al . Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work [J]. Engineering Applications of Computational Fluid Mechanics , 2018 , 12 ( 1 ): 411 - 437 .
Pradhan B , Tehrany M S , Jebur M N . A new semiautomated detection mapping of flood extent from TerraSAR-X satellite image using rule-based classification and taguchi optimization techniques [J]. IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 7 ): 4331 - 4342 .
Raspini F , Bardi F , Bianchini S , et al . The contribution of satellite SAR-derived displacement measurements in landslide risk management practices [J]. Natural Hazards , 2017 , 86 ( 1 ): 327 - 351 .
Liou Y A , Kar S K , Chang L Y . Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: a study following the 12 May 2008 Wenchuan Earthquake [J]. International Journal of Remote Sensing , 2010 , 31 ( 13 ): 3355 - 3368 .
唐川 , 张军 , 万石云 , 等 . 基于高分辨率遥感影象的城市泥石流灾害损失评估 [J]. 地理科学 , 2006 , 26 ( 3 ): 358 - 363 .
Tang Ch , Zhang J , Wan Sh Y , et al . Loss evaluation of urban debris flow hazard using high spatial resolution satellite imagery [J]. Scientia Geographica Sinica , 2006 , 26 ( 3 ): 358 - 363 . (in Chinese)
Ofli F , Meier P , Imran M , et al . Combining human computing and machine learning to make sense of big (aerial) data for disaster response [J]. Big Data , 2016 , 4 ( 1 ): 47 - 59 .
Foresti G L , Farinosi M , Vernier M . Situational awareness in smart environments: socio-mobile and sensor data fusion for emergency response to disasters [J]. Journal of Ambient Intelligence & Humanized Computing , 2015 , 6 ( 2 ): 239 - 257 .
Kakooei M , Baleghi Y . Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment [J]. International Journal of Remote Sensing , 2017 , 38 ( 8-10 ): 2511 - 2534 .
Moya L , Yamazaki F , Liu W , et al . Detection of collapsed buildings due to the 2016 Kumamoto, Japan, earthquake from Lidar data [J]. Natural Hazards and Earth System Sciences Discussions , 2017 , 18 : 65 - 78 .
Chen D , Liu Z X , Wang L Z , et al . Natural disaster monitoring with wireless sensor networks: a case study of data-intensive applications upon low-cost scalable systems [J]. Mobile Networks and Applications , 2013 , 18 ( 5 ): 651 - 663 .
Büscher M , Liegl M , Thomas V . Collective Intelligence in Crises [M]. Switzerland : Springer , 2014 : 243 - 265 .
陈志勇 . 智能电网的大数据处理技术应用 [J]. 集成电路应用 , 2020 , 37 ( 2 ): 78 - 79 .
Chen Zh Y . Application of big data processing technology in smart grid [J]. Application of IC , 2020 , 37 ( 2 ): 78 - 79 . (in Chinese)
Konopko J . Distributed and parallel approach for handle and perform huge datasets [C]∥ International Conference of Computational Methods in Sciences and Engineering 2015 (ICCMSE 2015) . Melville, NY : AIP Publishing LLC , 2015 .
孟小峰 , 慈祥 . 大数据管理:概念、技术与挑战 [J]. 计算机研究与发展 , 2013 , 50 ( 1 ): 146 - 169 .
Meng X F , Ci X . Big data management:concepts, techniques and challenges [J]. Journal of Computer Research and Development , 2013 , 50 ( 1 ): 146 - 169 . (in Chinese)
覃雄派 , 王会举 , 李芙蓉 , 等 . 数据管理技术的新格局 [J]. 软件学报 , 2013 , 24 ( 2 ): 175 - 197 .
Qin X P , Wang H J , Li F R , et al . New landscape of data management technologies [J]. Journal of Software , 2013 , 24 ( 2 ): 175 - 197 . (in Chinese)
李绍俊 , 杨海军 , 黄耀欢 , 等 . 基于NoSQL数据库的空间大数据分布式存储策略 [J]. 武汉大学学报(信息科学版) , 2017 , 42 ( 2 ): 163 - 169 .
Li Sh J , Yang H J , Huang Y H , et al . Geo-spatial big data storage based on NoSQL database [J]. Geomatics and Information Science of Wuhan University , 2017 , 42 ( 2 ): 163 - 169 . (in Chinese)
Pavlo A , Aslett M . What's really new with NewSQL? [J]. Sigmod Record: ACM SIGMOD (Management of Data) , 2016 , 45 ( 2 ): 45 - 55 .
徐述 , 汪彦 , 曾海洋 , 等 . 大数据应用下的新型分布式数据库NewSQL [J]. 数字技术与应用 , 2018 , 36 ( 8 ): 51 - 52 .
Xu Sh , Wang Y , Zeng H Y , et al . New distributed database NewSQL based on big data application [J]. Digital Technology &Application , 2018 , 36 ( 8 ): 51 - 52 . (in Chinese)
郭雷风 . 面向农业领域的大数据关键技术研究 [D]. 北京 : 中国农业科学院 , 2016 .
Guo L F . Study on the key technologies of big data for agriculture [D]. Beijing : Chinese Academy of Agricultural Sciences , 2016 . (in Chinese)
王光宏 , 蒋平 . 数据挖掘综述 [J]. 同济大学学报(自然科学版) , 2004 , 32 ( 2 ): 246 - 252 .
Wang G H , Jang P . Survey of data mining [J]. Journal of Tongji University Natural (Science) , 2004 , 32 ( 2 ): 246 - 252 . (in Chinese)
Dramsch J S . 70 years of machine learning in geoscience in review [J]. Advances in Geophysics , 2020 , 61 : 1 - 55 .
Krizhevsky A , Sutskever I , Hinton G E . Imagenet classification with deep convolutional neural networks [J]. Advances in Neural Information Processing Systems , 2012 , 25 ( 2 ): 1097 - 1105 .
Cumbane S P , Gidófalvi G . Review of big data and processing frameworks for disaster response applications [J]. International Journal of Geo-Information , 2019 , 8 ( 9 ): 387 .
Dittrich J , Quiané-RuizJorge-Arnulfo . Efficient big data processing in Hadoop MapReduce [J]. Proceedings of the VLDB Endowment , 2012 , 5 ( 12 ): 2014 - 2015 .
Abadi D J , Ahmad Y , Balazinska M , et al . The design of the borealis stream processing engine [J]. CIDR , 2005 , 5 : 277 - 289 .
Akidau T , Balikov A , Bekiroğlu K , et al . Millwheel: fault-tolerant stream processing at internet scale [J]. Proceedings of the Vldb Endowment , 2013 , 6 ( 11 ): 1033 - 1044 .
Ananthanarayanan R , Basker V , Das S , et al . Photon: Fault-tolerant and scalable joining of continuous data streams [C]∥ Acm Sigmod International Conference on Management of Data . New York : ACM , 2013 .
Gurusamy V , Kannan S , Nandhini K . The real time big data processing framework: Advantages and limitations [J]. International Journal of Computer Sciences and Engineering , 2017 , 5 ( 12 ): 305 - 312 .
Suryawanshi S R , Deshpande U L . Review of risk management for landslide forecasting, monitoring and prediction using wireless sensors network [C]∥ 2017 International Conference on Innovations in Information , Embedded and Communication Systems (ICIIECS). New York : IEEE , 2017 .
Atzeni C , Barla M , Pieraccini M , et al . Early warning monitoring of natural and engineered slopes with ground-based synthetic-aperture radar [J]. Rock Mechanics and Rock Engineering , 2015 , 48 ( 1 ): 235 - 246 .
Intrieri E , Gigli G , Mugnai F , et al . Design and implementation of a landslide early warning system [J]. Engineering Geology , 2012 , 147 : 124 - 136 .
Dong J , Zhang L , Tang M G , et al . Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China [J]. Remote Sensing of Environment , 2018 , 205 : 180 - 198 .
Barla M , Antolini F . An integrated methodology for landslides' early warning systems [J]. Landslides , 2016 , 13 ( 2 ): 215 - 228 .
Lazarescu M T . Design of a WSN platform for long-term environmental monitoring for IoT applications [J]. IEEE Journal on Emerging and Selected Topics in Circuits and Systems , 2013 , 3 ( 1 ): 45 - 54 .
Li J , Li C K , Li K , et al . Design of landslide monitoring and early warning system based on internet of things [J]. Applied Mechanics & Materials , 2014 , 511-512 : 197 - 201 .
Aggarwal S , Mishra P K , Sumakar K V S , et al . Landslide monitoring system implementing IOT using video camera [C]∥ 3rd International Conference for Convergence in Technology (I2CT) . New York : IEEE , 2018 .
Kansal A , Singh Y , Kumar N , et al . Detection of forest fires using machine learning technique: A perspective [C]∥ International Conference on Image Information Processing . New York : IEEE , 2015 .
Karunarathne S M , Dray M , Popov L , et al . A technological framework for data-driven IoT systems: Application on landslide monitoring [J]. Computer Communications , 2020 , 154 : 298 - 312 .
Zhu L , Huang L H , Fan L Y , et al . Landslide susceptibility prediction modeling based on remote sensing and a novel deep learning algorithm of a cascade-parallel recurrent neural network [J]. Sensors (Basel, Switzerland) , 2020 , 20 ( 6 ): 1576 .
Kang K H , Park H J . Study on the effect of training data sampling strategy on the accuracy of the landslide susceptibility analysis using random forest method [J]. Economic and Environmental Geology , 2019 , 52 ( 2 ): 199 - 212 .
Albano R , Sole A . Geospatial methods and tools for natural risk management and communications [J]. Isprs International Journal of Geo Information , 2018 , 7 ( 12 ): 470 .
Yousefi S , Pourghasemi H R , Emami S N , et al . A machine learning framework for multi-hazards modeling and mapping in a mountainous area [J]. Entific Reports , 2020 , 10 ( 1 ): 1 - 14 .
胡涛 , 樊鑫 , 王硕 , 等 . 基于逻辑回归模型和3S技术的思南县滑坡易发性评价 [J]. 地质科技通报 , 2020 , 39 ( 2 ): 113 - 121 .
Hu T , Fan X , Wang Sh , et al . Landslide susceptibility evaluation of Sinan County using logistics regression model and 3S technology [J]. Bulletin of Geological Science and Technology , 2020 , 39 ( 2 ): 113 - 121 . (in Chinese)
Zhang W G , Ching J Y , Goh A T C , et al . Big data and machine learning in geoscience and geoengineering: Introduction [J]. Geoscience Frontiers , 2021 , 12 ( 1 ): 327 - 329 .
Shafizadeh-Moghadam H , Minaei M , Shahabi H , et al . Big data in Geohazard: Pattern mining and large scale analysis of landslides in Iran [J]. Earth Science Informatics , 2019 , 12 ( 1 ): 1 - 17 .
Lee C Y , Huang J Q , Ma W P , et al . Analyze the rainfall of landslide on Apache Spark [C]∥ Proceedings of 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI) . New York : IEEE , 2018 : 348 - 351 .
Zhao J B , Liu Y X , Hu M . Optimisation algorithm for decision trees and the prediction of horizon displacement of landslides monitoring [J]. The Journal of Engineering , 2018 , 2018( 16 ): 1698 - 1703 .
赵久彬 , 刘元雪 , 刘娜 , 等 . 海量监测数据下分布式BP神经网络区域滑坡空间预测方法 [J]. 岩土力学 , 2019 , 40 ( 7 ): 2866 - 2872 .
Zhao J B , Liu Y X , Liu N , et al . Spatial prediction method of regional landslide based on distributed bp neural network algorithm under massive monitoring data [J]. Rock and Soil Mechanics , 2019 , 40 ( 7 ): 2866 - 2872 . (in Chinese)
Youssef A M , Pourghasemi H R . Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia [J]. Geoscience Frontiers , 2021 , 12 ( 2 ): 639 - 655 .
Ngo P T T , Panahi M , Khosravi K , et al . Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran [J]. Geoscience Frontiers , 2021 , 12 ( 2 ): 505 - 519 .
Xue D J , He Z W , Wang Z H . Zhouqu County 8.8 extra-large-scale debris flow characters of remote sensing image analysis [C]∥ 2011 International Conference on Electronics, Communications and Control (ICECC). New York : IEEE , 2011 : 597 - 600 .
Wen Q , He H X , Wang X F , et al . UAV remote sensing hazard assessment in Zhouqu debris flow disaster [C]∥ Remote Sensing of the Ocean , Sea Ice, Coastal Waters, & Large Water Regions. Bellingham , WA : Spie-Int Soc Optical Engineering , 2011 .
Lu H M , Nakashima S , Li Y J , et al . A fast debris flow disasters areas detection method of earthquake images in remote sensing system [J]. Disaster Advances , 2012 , 5 ( 4 ): 796 - 799 .
Yu H , Gan S , Yuan X P , et al . Remote sensing monitoring of debris flow area in Dabaini River Basin of Xiaojiang, Dongchuan County [C]∥ 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics) . New York : IEEE , 2018 : 1 - 6 .
Yin J Z , He F Q , Luo Z B . Researching the relationships between the environmental change of vegetation and the activity of debris flows based on remote sensing and GIS [J]. Procedia Environmental Sciences , 2011 , 11 : 918 - 924 .
Lee X Y , Lee K C . Risk assessment on debris flow hazard along linear construction civil engineering based on satellite remote sensing and fuzzy comprehensive evaluation method [C]∥International Conference on Chemical, Material and Food Engineering. Paris, France : Atlantis Press , 2015 .
Zhang X H , Gan S , Yuan X P , et al . Comprehensive analysis of characteristics of debris flow fans in xiaojiang valley by using remote sensing method [C]∥ 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) . New York : IEEE , 2019 .
Shah S H , Yaqoob I . A survey: Internet of things (IOT) technologies, applications and challenges [C]∥ 2016 IEEE Smart Energy Grid Engineering (SEGE) . New York : IEEE . DOI:10.1109/SEGE , 20167589556 : 381 - 385 .
Huang J , Huang R Q , Ju N P , et al . 3D WebGIS-based platform for debris flow early warning: A case study [J]. Engineering Geology , 2015 , 197 : 57 - 66 .
Ma H . Design and application on Bluetooth-based wireless sensor network debris flow mountain health monitoring system [C]∥ Advanced Materials Research . Stafa-Zurich, Switzerland : Trans Tech Publications Ltd , 2014 : 829 - 832 .
Ko H Y , Fang Y M , Chang Y H . Using mobile sensors for in-situ monitoring of debris flows in Taiwan [C]∥ 2009 17th International Conference on Geoinformatics . New York : IEEE , 2009 : 1 - 4 .
Ye J X , Kurashima Y , Kobayashi T , et al . An efficient in-situ debris flow monitoring system over a wireless accelerometer network [J]. Remote Sensing , 2019 , 11 ( 13 ): 1512 .
Lee H C , Banerjee A , Fang Y M , et al . Design of a multifunctional wireless sensor for in-situ monitoring of debris flows [J]. IEEE Transactions on Instrumentation and Measurement , 2010 , 59 ( 11 ): 2958 - 2967 .
Chiou I J , Chen C H , Liu W L , et al . Methodology of disaster risk assessment for debris flows in a river basin [J]. Stochastic Environmental Research and Risk Assessment , 2015 , 29 ( 3 ): 775 - 792 .
Xu W B , Jing S C , Yu W J , et al . A comparison between Bayes discriminant analysis and logistic regression for prediction of debris flow in Southwest Sichuan, China [J]. Geomorphology , 2013 , 201 : 45 - 51 .
张永宏 , 葛涛涛 , 田伟 , 等 . 基于地质大数据的泥石流灾害易发性评价 [J]. 计算机应用 , 2018 , 38 ( 11 ): 3319 - 3325 .
Zhang Y H , Ge T T , Tian W , et al . Evaluation of susceptibility to debris flow hazards based on geological big data [J]. Journal of Computer Applications , 2018 , 38 ( 11 ): 3319 - 3325 . (in Chinese)
Zhao Y , Meng X M , Qi T J , et al . AI-based identification of low-frequency debris flow catchments in the Bailong River basin, China [J]. Geomorphology , Doi: 10.1016/J.geomorph.2020.107125 http://dx.doi.org/10.1016/J.geomorph.2020.107125 .
Liang Z , Wang C M , Zhang Z M . A comparison of statistical and machine learning methods for debris flow susceptibility mapping [J]. Stochastic Environmental Research and Risk Assessment , 2020 , 34 ( 11 ): 1887 - 1907 .
张书豪 , 吴光 . 随机森林与GIS的泥石流易发性及可靠性 [J]. 地球科学 , 2019 , 44 ( 9 ): 3115 - 3134 .
Zhang Sh H , Wu G . Debris flow susceptibility and its reliability based on random forest and GIS [J]. Earth Science , 2019 , 44 ( 9 ): 3115 - 3134 . (in Chinese)
Frodella W , Ciampalini A , Gigli G , et al . Synergic use of satellite and ground based remote sensing methods for monitoring the San Leo rock cliff (Northern Italy) [J]. Geomorphology , 2016 , 264 : 80 - 94 .
Frodella W , Lombardi L , Nocentini M , et al . Ground based remote sensing techniques for the San Leo (northern Italy) rock cliff monitoring [J]. Rendiconti Online Societa Geologica Italiana , 2016 , 41 : 239 - 242 .
Ciampalini A , Raspini F , Frodella W . Back monitoring of the San Leo (northern Italy) rock cliff by means of SqueeSAR technique [J]. Rendiconti Online Societa Geologica Italiana , 2016 , 41 : 227 - 230 .
Gischig V , Amann F , Moore J R , et al . Composite rock slope kinematics at the current Randa instability, Switzerland, based on remote sensing and numerical modeling [J]. Engineering Geology , 2011 , 118 ( 1/2 ): 37 - 53 .
Mazzanti P , Brunetti A , Bretschneider A . A new approach based on terrestrial remote-sensing techniques for rock fall hazard assessment [M]. Modern Technologies for Landslide Monitoring and Prediction . Berlin, Heidelberg : Springer , 2015 : 69 - 87 .
Gigli G , Morelli S , Fornera S , et al . Terrestrial laser scanner and geomechanical surveys for the rapid evaluation of rock fall susceptibility scenarios [J]. Landslides , 2014 , 11 ( 1 ): 1 - 14 .
Nikolakopoulos K , Depountis N , Vagenas N , et al . Rockfall risk evaluation using geotechnical survey, remote sensing data, and GIS: A case study from western Greece [C]∥ Third International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2015) . Bellingham, Wa : Spie-Int Soc Optical Engineering , 2015 .
胡涛 , 樊鑫 , 王硕 , 等 . 基于径向基神经网络的思南县崩塌易发性评价 [J]. 科学技术与工程 , 2019 , 19 ( 35 ): 61 - 69 .
Hu T , Fan X , Wang Sh , et al . Collapse susceptibility assessment of sinan county based on radial basis function neural network [J]. Science Technology and Engineering , 2019 , 19 ( 35 ): 61 - 69 . (in Chinese)
Alippi C , Camplani R , Galperti C , et al . Effective design of WSNs: From the lab to the real world [C]∥ 2008 3rd International Conference on Sensing Technology . New York : IEEE , 2008 : 1 - 9 .
Alippi C , Camplani R , Galperti C , et al . An hybrid wireless-wired monitoring system for real-time rock collapse forecasting [C]∥ The 7th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2010) . New York : IEEE , 2010 : 224 - 231 .
Kato S , Kohashi H . Study on the monitoring system of slope failure using optical fiber sensors [C]∥ Geocongress 2006: Geotechnical Engineering in the Information Technology Age . Reston, VA : ASCE , 2006 : 1 - 6 .
Losasso L , Sdao F . The artificial neural network for the rockfall susceptibility assessment:A case study in Basilicata (Southern Italy) [J]. Geomatics , Natural Hazards and Risk, 2018 , 9 ( 1 ): 737 - 759 .
Liao X H , Wang X L , Li L H , et al . Engineering application and prediction of the influence area of the rockfall hazards [J]. Mathematical Problems in Engineering , 2020 , 2020 : 1 - 14 .
林报嘉 , 刘晓东 , 杨川 , 等 . XGBoost机器学习模型与GIS技术结合的公路崩塌灾害易发性研究 [J]. 公路 , 2020 , 65 ( 7 ): 20 - 26 .
Lin B J , Liu X D , Yang Ch , et al . Avalanche susceptibility assessment of highway based on xgboost machine learning model and GIS method [J]. Highway , 2020 , 65 ( 7 ): 20 - 26 . (in Chinese)
Chen L L , Zhang W G , Gao X C , et al . Design charts for reliability assessment of rock bedding slopes stability against bi-planar sliding: SRLEM and BPNN approaches [J]. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards , 2020 , 1 : 1 - 16 .
Fanos A M , Pradhan B , Mansor S , et al . A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data [J]. Landslides , 2018 , 15 ( 9 ): 1833 - 1850 .
Fanos A M , Pradhan B , Alamri A , et al . Machine learning-based and 3D kinematic models for rockfall hazard assessment using LiDAR data and GIS [J]. Remote Sensing , 2020 , 12 ( 11 ): 1755 .
Shi G , Li D . Automatic measurement and alarm prediction system of land subsidence [J]. Computer Automated Measurement & Control , 2003 , 11 ( 4 ): 244 - 246 .
An Z H , Wang H , Wu F , et al . The research of remote sensing in karst collapse remote sense based on airborne LiDAR system: Taking Meitanba mining area in Hunan Province as an example [J]. The International Society for Optical Engineering , 2014 , 9299 : 92990 Z-92990Z-6.
Yu T , Twumasi J O , Le V , et al . Surface and subsurface remote sensing of concrete structures using synthetic aperture radar imaging [J]. Journal of Structural Engineering , 2017 , 143 ( 10 ): 04017143 .
Tomás R , Romero R , Mulas J , et al . Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain [J]. Environmental Earth Sciences , 2014 , 71 ( 1 ): 163 - 181 .
Zhu B , Xia K . Design of Subsidence monitoring system based on wireless sensor networks [C]∥ International Conference on Wireless Communications Networking & Mobile Computing . New York : IEEE , 2010 : 1 - 4 .
Marturià J , Lopez F , Gigli G , et al . Integrating wireless sensor network for monitoring subsidence phenomena [C]∥ EGU General Assembly Conference Abstracts.[ S .l]: [s.n.] , 2016 .
Li C , Azzam R , Fernández-Steeger T M . Kalman filters in geotechnical monitoring of ground subsidence using data from MEMS sensors [J]. Sensors , 2016 , 16 ( 7 ): 1109 .
Zambrano A , Perez I , Palau C , et al . Quake detection system using smartphone-based wireless sensor network for early warning [C]∥ 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (Percom Workshops) . New York : IEEE , 2014 : 297 - 302 .
Schwegmann C P , Kleynhans W , Engelbrecht J , et al . Subsidence feature discrimination using deep convolutional neural networks in synthetic aperture radar imagery [C]∥ 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . New York : IEEE , 2017 : 4626 - 4629 .
Li Z G , Xiao S D , Pan Y H , et al . The hazard assessment of karst surface collapse risk zoning based on BP neural network in Wuhan City [J]. Applied Mechanics & Materials , 2013 , 405-408 : 2376 - 2379 .
Liu L , Wang C , Zhang H , et al . Automatic monitoring method for surface deformation of coastal area based on time series analysis [J]. Journal of Coastal Research , 2019 , 93 ( Sup 1 ): 194 - 199 .
Li T , Xing X , Shi Z . Neural network model based on genetic algorithm for predicating mining subsidence in multi-fault areas [C]∥ 2010 International Conference on Mine Hazards Prevention and Control . Paris, France : Advances in Intelligent Systems Research , 2010 : 474 .
Han D , Li X J . The surface subsidence prediction of shield construction based on the fuzzy neural network [C]∥ GeoShanghai International Conference . Singapore : Springer , 2018 : 190 - 197 .
Lv W , Wang M , Zhu X G . Model for prediction of surface subsidence coefficient in backfilled coal mining areas based on genetic algorithm and BP neural network [J]. Journal of Computational Methods in Sciences and Engineering , 2016 , 16 ( 4 ): 745 - 753 .
Yang W F , Xia X H . Prediction of mining subsidence under thin bedrocks and thick unconsolidated layers based on field measurement and artificial neural networks [J]. Computers & Geosciences , 2013 , 52 : 199 - 203 .
Wu M H , Xia X G . Study on the calculation of surface subsidence coefficient based on principal component analysis and neural networks [C]∥ 2nd Annual International Conference on Energy , Environmental & Sustainable Ecosystem Development (EESED 2016 ). Paris, France: Atlantis Press , 2016 .
Zhou Q H , Hu Q W , Ai M Y , et al . An improved GM (1, 3) model combining terrain factors and neural network error correction for urban land subsidence prediction [J]. Geomatics , Natural Hazards and Risk, 2020 , 11 ( 1 ): 212 - 229 .
Zhang W G , Zhang R H , Wu C Z , et al . State-of-the-art review of soft computing applications in underground excavations [J]. Geoscience Frontiers , 2020 , 11 ( 4 ): 1095 - 1106 .
Zhang W G , Li H R , Wu C Z , et al . Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling [J]. Underground Space , 2021 , 6 ( 4 ): 353 - 363 .
Goh A T C , Zhang W G , Zhang Y M , et al . Determination of earth pressure balance tunnel-related maximum surface settlement: A multivariate adaptive regression splines approach [J]. Bulletin of Engineering Geology and the Environment , 2018 , 77 ( 2 ): 489 - 500 .
Zhang W G , Goh A T C . Multivariate adaptive regression splines for analysis of geotechnical engineering systems [J]. Computers and Geotechnics , 2013 , 48 : 82 - 95 .
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