Multi-hazard disaster studies: Monitoring, detection, recovery, and management, based on emerging technologies and optimal techniques

Abstract Every year man-made and natural disasters impact the lives of millions of people. The frequency of occurrence of such disasters is steadily increasing since the last 50 years, and this has resulted in considerable loss of life, destruction of infrastructure, and social and economic disruption. A focussed and comprehensive solution is needed encompassing all aspects, including early detection of disaster scenarios, prevention, recovery, and management to minimize the losses. This survey paper presents a critical analysis of the existing methods and technologies that are relevant to a disaster scenario, such as WSN, remote sensing technique, artificial intelligence, IoT, UAV, and satellite imagery, to encounter the issues associated with disaster monitoring, detection, and management. In case of emergency conditions arising out of a typical disaster scenario, there is a strong likelihood that the communication networks will be partially disrupted; thus the alternate networks can play a vital role in disaster detection and management. It focuses on the role of the alternate networks and the associated technologies in maintaining connectivity in various disaster scenarios. It presents a comprehensive study on multiple disasters such as landslide, forest fire, and an earthquake based on the latest technologies to monitor, detect, and manage the various disasters. It focuses on several parameters that are necessary for disaster detection and monitoring and offers appropriate solutions. It also touches upon big data analytics for disaster management. Several techniques are explored, along with their merits and demerits. Open challenges are highlighted, and possible future directions are given.

[1]  S. R. Vijayalakshmi,et al.  Real Time Monitoring of Wireless Fire Detection Node , 2016 .

[2]  Reinhardt Euler,et al.  Boundaries and Hulls of Euclidean Graphs: From Theory to Practice , 2018 .

[3]  Shoab A. Khan,et al.  A Cross-Layer Design for a Multihop, Self-Healing, and Self-Forming Tactical Network , 2019, Wirel. Commun. Mob. Comput..

[4]  Jiann-Yeou Rau,et al.  Combining Unmanned Aerial Vehicles, and Internet Protocol Cameras to Reconstruct 3-D Disaster Scenes During Rescue Operations , 2018, Prehospital emergency care : official journal of the National Association of EMS Physicians and the National Association of State EMS Directors.

[5]  Murat Dener,et al.  Fire Detection Systems in Wireless Sensor Networks , 2015 .

[6]  Guoqiang Wang,et al.  Multi-UAV Rapid-Assessment Task-Assignment Problem in a Post-Earthquake Scenario , 2019, IEEE Access.

[7]  Timothy W. McLain,et al.  Cooperative forest fire surveillance using a team of small unmanned air vehicles , 2006, Int. J. Syst. Sci..

[8]  Sajal K. Das,et al.  Coverage and Connectivity Issues in Wireless Sensor Networks , 2005 .

[9]  B. Pradhan,et al.  Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey) , 2012, Environmental Monitoring and Assessment.

[10]  Chi Harold Liu,et al.  The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey , 2015, IEEE Transactions on Emerging Topics in Computing.

[11]  B. Pham,et al.  Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods , 2017, Theoretical and Applied Climatology.

[12]  John D. Bolten,et al.  Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska , 2019, Remote. Sens..

[13]  Andrea Giorgetti,et al.  Design and deployment of a wireless sensor network for landslide risk management , 2014, 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[14]  David E. Culler,et al.  TOSSIM: accurate and scalable simulation of entire TinyOS applications , 2003, SenSys '03.

[15]  Sally McClean,et al.  Unmanned Aerial Vehicles for Disaster Management , 2018, Springer Natural Hazards.

[16]  Robert Tappan Morris,et al.  Span: An Energy-Efficient Coordination Algorithm for Topology Maintenance in Ad Hoc Wireless Networks , 2002, Wirel. Networks.

[17]  Kelly T. Morrison,et al.  Rapidly recovering from the catastrophic loss of a major telecommunications office , 2011, IEEE Communications Magazine.

[18]  Xiaoqiao Meng,et al.  Real-time forest fire detection with wireless sensor networks , 2005, Proceedings. 2005 International Conference on Wireless Communications, Networking and Mobile Computing, 2005..

[19]  Maneesha V. Ramesh,et al.  Real-Time Wireless Sensor Network for Landslide Detection , 2009, 2009 Third International Conference on Sensor Technologies and Applications.

[20]  Babar Nazir,et al.  Energy efficient and QoS aware routing protocol for Clustered Wireless Sensor Network , 2013, Comput. Electr. Eng..

[21]  Youmin Zhang,et al.  Vision-based forest fire detection in aerial images for firefighting using UAVs , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[22]  Hiroyuki Morikawa,et al.  A high-density earthquake monitoring system using wireless sensor networks , 2007, SenSys '07.

[23]  Xu Zhang,et al.  Localization Applications of 3D-GIS Artificial Weather Modification Operational Command System in Fuxin, China , 2019 .

[24]  Andrew P Hunt,et al.  Using an Unmanned Aircraft System (Drone) to Conduct a Complex High Altitude Search and Rescue Operation: A Case Study , 2019, Wilderness & environmental medicine.

[25]  Raphael C.-W. Phan,et al.  Maximise unsafe path routing protocol for forest fire monitoring system using Wireless Sensor Networks , 2012, 2012 IEEE 3rd International Conference on Networked Embedded Systems for Every Application (NESEA).

[26]  Franco Zambonelli,et al.  Landslide monitoring with sensor networks: experiences and lessons learnt from a real-world deployment , 2011, Int. J. Sens. Networks.

[27]  Manijeh Keshtgary,et al.  Performance evaluation of routing protocols for wireless sensor networks in forest fire detection application , 2013, The 5th Conference on Information and Knowledge Technology.

[28]  A. Zhu,et al.  Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China) , 2019, Environmental Earth Sciences.

[29]  Anand S. Bhosle,et al.  Forest disaster management with wireless sensor network , 2016, 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT).

[30]  Michail N. Giannakos,et al.  Big data analytics capabilities: a systematic literature review and research agenda , 2017, Information Systems and e-Business Management.

[31]  Stavros A. Koubias,et al.  Architecture Design and Implementation of an Ad-Hoc Network for Disaster Relief Operations , 2007, IEEE Transactions on Industrial Informatics.

[32]  Steven E. Collier The Emerging Enernet: Convergence of the Smart Grid with the Internet of Things , 2017 .

[33]  I. Towhata Geotechnical Earthquake Engineering , 2008 .

[34]  Mohd. Samar Ansari,et al.  Tethered Balloon Technology in Design Solutions for Rescue and Relief Team Emergency Communication Services , 2018, Disaster Medicine and Public Health Preparedness.

[35]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[36]  Naveed Riaz,et al.  Ad hoc wireless Sensor Network Architecture for Disaster Survivor Detection , 2011 .

[37]  Vandana Mohindru,et al.  Detection of forest fires using machine learning technique: A perspective , 2015, 2015 Third International Conference on Image Information Processing (ICIIP).

[38]  Dhananjai Pandey,et al.  Earthquake - A Natural Disaster, Prediction, Mitigation, Laws and Government Policies, Impact on Biogeochemistry of Earth Crust, Role of Remote Sensing and GIS in Management in India - An Overview , 2019 .

[39]  C. Benson,et al.  Economic and Financial Impacts of Natural Disasters: an Assessment of Their Effects and Options for Mitigation: Synthesis Report , 2003 .

[40]  J. Chris Oberg,et al.  Disasters will happen - are you ready? , 2011, IEEE Communications Magazine.

[41]  Irshad Khan,et al.  A Smart IoT Device for Detecting and Responding to Earthquakes , 2019, Electronics.

[42]  Manzhu Yu,et al.  Big Data in Natural Disaster Management: A Review , 2018 .

[43]  Eleni Chatzi,et al.  Vibration monitoring via spectro-temporal compressive sensing for wireless sensor networks , 2017, Life-Cycle of Structural Systems.

[44]  Shyama Gupta,et al.  EARTHQUAKE DISASTERS IN HILLY AREAS (CASE STUDY UTTARAKHAND) –Part II , 2016 .

[45]  M. Tahar Kechadi,et al.  CupCarbon: a multi-agent and discrete event wireless sensor network design and simulation tool , 2014, SimuTools.

[46]  Shuangcheng Zhang,et al.  Ground-Based GPS Used for Snowfall Weather Monitoring Research , 2019, Lecture Notes in Electrical Engineering.

[47]  Lei Zhang,et al.  A Fair Energy Conserving Routing Algorithm for Wireless Sensor Networks , 2004, EUSAI.

[48]  Patricia Morreale,et al.  Mobile ad hoc network communication for disaster recovery , 2015, Int. J. Space Based Situated Comput..

[49]  Aníbal Ollero,et al.  Journal of Intelligent & Robotic Systems manuscript No. (will be inserted by the editor) An Unmanned Aircraft System for Automatic Forest Fire Monitoring and Measurement , 2022 .

[50]  Hyun-Woo Lee,et al.  Deep neural networks for wild fire detection with unmanned aerial vehicle , 2017, 2017 IEEE International Conference on Consumer Electronics (ICCE).

[51]  S. R. Vijayalakshmi,et al.  A survey of Internet of Things in fire detection and fire industries , 2017, 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC).

[52]  Matthew Butler,et al.  Efficient IoT-enabled Landslide Monitoring , 2019, 2019 IEEE 5th World Forum on Internet of Things (WF-IoT).

[53]  A. Varga,et al.  Using the OMNeT++ discrete event simulation system in education , 1999 .

[54]  R. O. Hayes,et al.  Detection, identification, and classification of mosquito larval habitats using remote sensing scanners in earth-orbiting satellites. , 1985, Bulletin of the World Health Organization.

[55]  Jens Palsberg,et al.  Avrora: scalable sensor network simulation with precise timing , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[56]  Emiliya Velizarova,et al.  Post-fire forest disturbance monitoring using remote sensing data and spectral indices , 2019, International Conference on Remote Sensing and Geoinformation of Environment.

[57]  Loretta Ichim,et al.  A Collaborative UAV-WSN Network for Monitoring Large Areas , 2018, Sensors.

[58]  Teerawat Issariyakul,et al.  Introduction to Network Simulator NS2 , 2008 .

[59]  Kamran Ali,et al.  A WSN for Monitoring and Event Reporting in Underground Mine Environments , 2018, IEEE Systems Journal.

[60]  Antonio-Javier Gallego,et al.  Detection of bodies in maritime rescue operations using unmanned aerial vehicles with multispectral cameras , 2018, J. Field Robotics.

[61]  Qing Miao,et al.  Are We Adapting to Floods? Evidence from Global Flooding Fatalities , 2018, Risk analysis : an official publication of the Society for Risk Analysis.

[62]  Guofeng Cao,et al.  How Do Cities Flow in an Emergency? Tracing Human Mobility Patterns during a Natural Disaster with Big Data and Geospatial Data Science , 2019, Urban Science.

[63]  Praveen Kumar Mishra,et al.  Landslide Monitoring System Implementing IOT Using Video Camera , 2018, 2018 3rd International Conference for Convergence in Technology (I2CT).

[64]  Janusz Wasowski,et al.  New Tools and Techniques of Remote Sensing for Geologic Hazard Assessment , 2018, Recent Advances in Geo-Environmental Engineering, Geomechanics and Geotechnics, and Geohazards.

[65]  Weidang Lu,et al.  UAV-Assisted Emergency Networks in Disasters , 2019, IEEE Wireless Communications.

[66]  Dimitrios Zekkos,et al.  Implementation of UAV localization methods for a mobile post-earthquake monitoring system , 2015, 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) Proceedings.

[67]  Yi Deng,et al.  urvey of data management and analysis in disaster situations , 2010 .

[68]  Liliana Enciso Quispe,et al.  Assessment of Throughput Performance under NS2 in Mobile Ad Hoc Networks (MANETs) , 2013, 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks.

[69]  Trina S. Myers,et al.  Riskr: a web 2.0 platform to monitor and share disaster information , 2015, Int. J. Grid Util. Comput..

[70]  Yigang Wei,et al.  An evaluation model for urban carrying capacity: A case study of China\'s mega-cities , 2016 .

[71]  Boleslaw K. Szymanski,et al.  SENSE: A WIRELESS SENSOR NETWORK SIMULATOR , 2005 .

[72]  Saeed Golian,et al.  Monitoring deforestation in Iran, Jangal-Abr Forest using multi-temporal satellite images and spectral mixture analysis method , 2016, Arabian Journal of Geosciences.

[73]  Youxian Sun,et al.  Energy efficient medium access control protocols for wireless sensor networks and its state-of-art , 2004, 2004 IEEE International Symposium on Industrial Electronics.

[74]  Gabriel González,et al.  Earthquake damage assessment for deterministic scenarios in Iquique, Chile , 2018, Natural Hazards.

[75]  Edison Pignaton de Freitas,et al.  A Service-Oriented Middleware for Integrated Management of Crowdsourced and Sensor Data Streams in Disaster Management † , 2018, Sensors.

[76]  Naveen Chauhan,et al.  Forest Fire Detection System Using IoT and Artificial Neural Network , 2018, International Conference on Innovative Computing and Communications.

[77]  Ran Goldblatt,et al.  Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States (Haiti, Dominica and St. Lucia) , 2020, Remote. Sens..

[78]  Rogelio Lozano,et al.  Autonomous Navigation for Unmanned Underwater Vehicles: Real-Time Experiments Using Computer Vision , 2019, IEEE Robotics and Automation Letters.

[79]  S. Johny Samuael,et al.  Geospatial Flood Risk Mapping and Analysis Tool , 2019 .

[80]  Xiaoyu Chen,et al.  Unmanned Aerial Vehicle for Remote Sensing Applications - A Review , 2019, Remote. Sens..

[81]  Quoc-Tuan Vien,et al.  Disaster management communication networks: Challenges and architecture design , 2015, 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops).

[82]  Wei Chen,et al.  Epidemic risk analysis after the Wenchuan Earthquake using remote sensing , 2010 .

[83]  Dirk Pesch,et al.  Environmental monitoring aware routing: making environmental sensor networks more robust , 2010, Telecommun. Syst..

[84]  Angelos Amditis,et al.  Integration of satellite and LTE for disaster recovery , 2015, IEEE Communications Magazine.

[85]  I. Yilmaz Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine , 2010 .

[86]  Ahmad A. A. Alkhatib A Review on Forest Fire Detection Techniques , 2014, Int. J. Distributed Sens. Networks.

[87]  Yong Wang,et al.  A survey of security issues in wireless sensor networks , 2006, IEEE Communications Surveys & Tutorials.

[88]  Maneesha Vinodini Ramesh,et al.  Data Reduction and Energy Sustenance in Multisensor Networks for Landslide Monitoring , 2014, IEEE Sensors Journal.

[89]  D L Butler,et al.  The Use of Wide Area Computer Networks in Disaster Management and the Implications for Hospital/Medical Networks , 1992, Annals of the New York Academy of Sciences.

[90]  Özgür Ulusoy,et al.  A framework for use of wireless sensor networks in forest fire detection and monitoring , 2012, Comput. Environ. Urban Syst..

[91]  Giovanni B. Crosta,et al.  Failure forecast for large rock slides by surface displacement measurements , 2003 .

[92]  Hyoung Woo Kim Development of Wireless Sensor Node for Landslide Detection , 2016 .

[93]  Liu Shunqing,et al.  Earthquake response analysis of soil-rock slope based on distribution of rocks , 2018 .

[94]  Richard Han,et al.  FireWxNet: a multi-tiered portable wireless system for monitoring weather conditions in wildland fire environments , 2006, MobiSys '06.

[95]  Ali Mirza Mahmood,et al.  Energy-Aware Reliable Routing by Considering Current Residual Condition of Nodes in MANETs , 2019 .

[96]  Azadeh Haghi,et al.  Integration of LiDAR and multispectral images for rapid exposure and earthquake vulnerability estimation. Application in Lorca, Spain , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[97]  Harkiran Kaur,et al.  Cloud-assisted green IoT-enabled comprehensive framework for wildfire monitoring , 2019, Cluster Computing.

[98]  Byung-rak Son,et al.  A Design and Implementation of Forest-Fires Surveillance System based on Wireless Sensor Networks for South Korea Mountains , 2006 .

[99]  Pooja Vengurlekar,et al.  WSN-Life Enhancing Routing Algorithm , 2014 .

[100]  Dianhong Wang,et al.  Anomaly Detection and Visual Perception for Landslide Monitoring Based on a Heterogeneous Sensor Network , 2017, IEEE Sensors Journal.

[101]  Maozhen Li,et al.  CLOTHO: A Large-Scale Internet of Things-Based Crowd Evacuation Planning System for Disaster Management , 2018, IEEE Internet of Things Journal.

[102]  Qunying Huang,et al.  A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data , 2017, Comput. Environ. Urban Syst..

[103]  Mukesh A. Zaveri,et al.  Resource Scheduling for Postdisaster Management in IoT Environment , 2019, Wirel. Commun. Mob. Comput..

[104]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[105]  Theodoros Anagnostopoulos,et al.  IoT-Enabled Ambulances Assisting Citizens' Well-Being after Earthquake Disasters in Smart Cities , 2019, 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[106]  Omprakash Kaiwartya,et al.  A reliable energy-efficient pressure-based routing protocol for underwater wireless sensor network , 2017, Wireless Networks.

[107]  I. Burud,et al.  Application of unmanned aerial vehicles in earth resources monitoring: focus on evaluating potentials for forest monitoring in Ethiopia , 2018 .

[108]  G. Simon,et al.  Simulation-based optimization of communication protocols for large-scale wireless sensor networks , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[109]  Michal Król,et al.  Wireless Sensor Networks and Multi-UAV systems for natural disaster management , 2017, Comput. Networks.

[110]  Luis Merino,et al.  Cooperative Unmanned Aerial Systems for Fire Detection, Monitoring, and Extinguishing , 2015 .

[111]  Francisco Martínez-Álvarez,et al.  Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure , 2017, Comput. Geosci..

[112]  Liliana Enciso Quispe,et al.  Behavior of Ad Hoc routing protocols, analyzed for emergency and rescue scenarios, on a real urban area , 2014, Expert Syst. Appl..

[113]  Muhammad Umair Hassan,et al.  DEAR-2: An Energy-Aware Routing Protocol with Guaranteed Delivery in Wireless Ad-hoc Networks , 2019, Recent Trends and Advances in Wireless and IoT-enabled Networks.

[114]  Yongsheng Yang,et al.  Message forwarding for WSN-Assisted Opportunistic Network in disaster scenarios , 2019, J. Netw. Comput. Appl..

[115]  Fahad Taha AL-Dhief,et al.  Performance Evaluation of LAR and OLSR Routing Protocols in Forest Fire Detection using Mobile Ad-Hoc Network , 2016 .

[116]  F. Antolini,et al.  Early Warning Monitoring of Natural and Engineered Slopes with Ground-Based Synthetic-Aperture Radar , 2014, Rock Mechanics and Rock Engineering.

[117]  Kaoru Sezaki,et al.  Capturing People Mobility with Mobile Sensing Technology for Disaster Evacuation , 2019, HCI.

[118]  Adnan Akhunzada,et al.  Exploring IoT Applications for Disaster Management: Identifying Key Factors and Proposing Future Directions , 2019, Recent Trends and Advances in Wireless and IoT-enabled Networks.

[119]  C. Elvidge,et al.  Monitoring forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System nighttime satellite data , 2006 .

[120]  Marco Chiani,et al.  A Robust Wireless Sensor Network for Landslide Risk Analysis: System Design, Deployment, and Field Testing , 2016, IEEE Sensors Journal.

[121]  Qixing Zhang,et al.  Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images , 2018 .

[122]  D. Laefer,et al.  The Need for Baseline Data Characteristics for GIS-Based Disaster Management Systems , 2006 .

[123]  Jui-Yi Ho,et al.  Assessment of susceptibility to rainfall-induced landslides using improved self-organizing linear output map, support vector machine, and logistic regression , 2017 .

[124]  Renjie Huang,et al.  Quality-Driven Volcanic Earthquake Detection Using Wireless Sensor Networks , 2010, 2010 31st IEEE Real-Time Systems Symposium.

[125]  Walid Saad,et al.  A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems , 2018, IEEE Communications Surveys & Tutorials.

[126]  Ou Ma,et al.  Performance optimization of tethered balloon technology for public safety and emergency communications , 2020, Telecommun. Syst..

[127]  S. Baruah,et al.  Seismic vulnerability assessment of earthquake-prone mega-city Shillong, India using geophysical mapping and remote sensing , 2020, Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards.

[128]  Xiaoguang Fan,et al.  A Clustering Routing Protocol for Mobile Ad Hoc Networks , 2016 .

[129]  Kechar Bouabdellah,et al.  Using Wireless Sensor Networks for Reliable Forest Fires Detection , 2013, ANT/SEIT.

[130]  Subhas Chandra Mukhopadhyay,et al.  Towards the Implementation of IoT for Environmental Condition Monitoring in Homes , 2013, IEEE Sensors Journal.

[131]  Paola Ceresa,et al.  Seismic Vulnerability Assessment of the Urban Building Environment in Nablus, Palestine , 2018 .

[132]  Yang Ran,et al.  Considerations and suggestions on improvement of communication network disaster countermeasures after the wenchuan earthquake , 2011, IEEE Communications Magazine.

[133]  Sergey V. Samsonov,et al.  A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters , 2009 .

[134]  Yuei-An Liou,et al.  Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: a study following the 12 May 2008 Wenchuan Earthquake , 2010 .

[135]  Dino Pedreschi,et al.  Data science at SoBigData: the European research infrastructure for social mining and big data analytics , 2018, International Journal of Data Science and Analytics.

[136]  Luis Merino,et al.  Multi-UAV Experiments: Application to Forest Fires , 2007 .

[137]  Mihai T. Lazarescu,et al.  Design of a WSN Platform for Long-Term Environmental Monitoring for IoT Applications , 2013, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[138]  Yu Gu,et al.  A Novel Accurate Forest Fire Detection System Using Wireless Sensor Networks , 2011, 2011 Seventh International Conference on Mobile Ad-hoc and Sensor Networks.

[139]  P. Mehta,et al.  Distributed Detection for Landslide Prediction using Wireless Sensor Network , 2007, 2007 First International Global Information Infrastructure Symposium.

[140]  Pongpisit Wuttidittachotti,et al.  Realistic propagation effects on wireless sensor networks for landslide management , 2019, EURASIP J. Wirel. Commun. Netw..

[141]  David M. Doolin,et al.  Wireless sensors for wildfire monitoring , 2005, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[142]  Kirankumar Momaya Role of Communication Networks in Network Organizations: A Case of Global Consultancy Firms , 1999 .

[143]  Ahcène Bounceur,et al.  Modeling Interference for Wireless Sensor Network Simulators , 2017 .

[144]  Francis Y. Enomoto,et al.  The Ikhana unmanned airborne system (UAS) western states fire imaging missions: from concept to reality (2006–2010) , 2011 .

[145]  Chuang Wang,et al.  Monitoring and Warning for Digital Twin-driven Mountain Geological Disaster , 2019, 2019 IEEE International Conference on Mechatronics and Automation (ICMA).

[146]  Arun Madhu,et al.  IoT Based Landslide Disaster Management System , 2020 .

[147]  Edward J. Williams,et al.  DInSAR technique for slow-moving landslide monitoring based on slope units , 2019 .

[148]  Jiun Ting Ding,et al.  Developing an energy-efficient and low-delay wake-up wireless sensor network-based structural health monitoring system using on-site earthquake early warning system and wake-on radio , 2019 .

[149]  Dirk Helbing,et al.  Disasters as Extreme Events and the Importance of Network Interactions for Disaster Response Management , 2006, Extreme Events in Nature and Society.

[150]  Xue Li,et al.  Location correction technique based on mobile communication base station for earthquake population heat map , 2018 .

[151]  Maneesha Vinodini Ramesh,et al.  Energy Efficient Data Acquisition Techniques Using Context Aware Sensing for Landslide Monitoring Systems , 2017, IEEE Sensors Journal.

[152]  Gokhan Izbirak,et al.  Post-earthquake response by small UAV helicopters , 2016, Natural Hazards.

[153]  Daniel Hein,et al.  An Integrated Rapid Mapping System for Disaster Management , 2017 .

[154]  N. Koteswar Rao Performance Comparison and Analysis of DSDV and AODV for MANET , 2010 .

[155]  B. Pradhan,et al.  A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility , 2017 .

[156]  Wei Chen,et al.  Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques , 2017 .

[157]  Michele Calvello,et al.  A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils , 2017 .

[158]  Debarati Guha-Sapir,et al.  Information systems and needs assessment in natural disasters: An approach for better disaster relief management. , 1986, Disasters.

[159]  Pierluigi Ritrovato,et al.  IoT and semantic web technologies for event detection in natural disasters , 2018, Concurr. Comput. Pract. Exp..

[160]  Paraskevas Tsangaratos,et al.  Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size , 2016 .

[161]  Bin Wang,et al.  Fundamental challenge in simulation and prediction of summer monsoon rainfall , 2005 .

[162]  Mahsa Salehi,et al.  A large-scale spatio-temporal data analytics system for wildfire risk management , 2017, GeoRich '17.

[163]  Leonhard Reindl,et al.  Event Monitoring in Emergency Scenarios Using Energy Efficient Wireless Sensor Nodes for the Disaster Information Management , 2016 .

[164]  Irfan-Ullah Awan,et al.  Analysis of GSM/GPRS Cell with Multiple Data Service Classes , 2003, Wirel. Pers. Commun..

[165]  Iwao Sasase,et al.  Priority based routing for forest fire monitoring in wireless sensor network , 2014 .

[166]  Yi-Han Xu,et al.  An Environmentally Aware Scheme of Wireless Sensor Networks for Forest Fire Monitoring and Detection , 2018, Future Internet.

[167]  Nicola Casagli,et al.  Design and implementation of a landslide early warning system , 2012 .

[168]  G. Athisha,et al.  Energy Efficient Modulation Techniques for Fault Tolerant Two-Tiered Wireless Sensor Networks , 2012 .

[169]  Erik Eberhardt,et al.  FROM CAUSE TO EFFECT: USING NUMERICAL MODELLING TO UNDERSTAND ROCK SLOPE INSTABILITY MECHANISMS , 2006 .

[170]  Ditipriya Sinha,et al.  Semisupervised Classification Based Clustering Approach in WSN for Forest Fire Detection , 2019, Wireless Personal Communications.

[171]  T. Banu,et al.  The Use of Drones in Forestry , 2016 .

[172]  Hasein Issa Sigiuk,et al.  Performance Evaluation of Dynamic Source Routing Protocol (DSR) on WSN , 2012 .

[173]  Ahcène Bounceur,et al.  CupCarbon: A new platform for the design, simulation and 2D/3D visualization of radio propagation and interferences in IoT networks , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[174]  Hamid Reza Pourghasemi,et al.  Erratum to: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia , 2016, Landslides.

[175]  Harold H. Szu,et al.  Smartphones, Grounds, Satellites, UAVs for Earthquake Nowcast , 2019, 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC).

[176]  Ruixi Yuan,et al.  A Novel Load Balanced and Lifetime Maximization Routing Protocol in Wireless Sensor Networks , 2008, VTC Spring 2008 - IEEE Vehicular Technology Conference.

[177]  Gail M. Atkinson,et al.  Earthquake Ground-Motion Prediction Equations for Eastern North America , 2006 .

[178]  Tolga Coplu,et al.  SENDROM: Sensor networks for disaster relief operations management , 2007, Wirel. Networks.

[179]  Hui Li,et al.  Natural Disaster Monitoring with Wireless Sensor Networks: A Case Study of Data-intensive Applications upon Low-Cost Scalable Systems , 2013, Mob. Networks Appl..

[180]  Kazuya Kaku,et al.  Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia , 2019, International Journal of Disaster Risk Reduction.

[181]  Wei Chen,et al.  A GIS-based comparative study of Dempster-Shafer, logistic regression and artificial neural network models for landslide susceptibility mapping , 2017 .

[182]  Iftekharul Mobin,et al.  An Intelligent Fire Detection and Mitigation System Safe from Fire (SFF) , 2016 .

[183]  Kotish Grover,et al.  WSN-Based System for Forest Fire Detection and Mitigation , 2019, Lecture Notes on Multidisciplinary Industrial Engineering.

[184]  Faisal Saeed,et al.  IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety , 2018, J. Sens. Actuator Networks.

[185]  Zhou Huang,et al.  A Spark-Based High Performance Computational Approach for Simulating Typhoon Wind Fields , 2018, IEEE Access.

[186]  Song Guo,et al.  Big Data Analytics for Emergency Communication Networks: A Survey , 2016, IEEE Communications Surveys & Tutorials.

[187]  Álvaro Araujo,et al.  Forest Monitoring and Wildland Early Fire Detection by a Hierarchical Wireless Sensor Network , 2016, J. Sensors.

[188]  Shahriar Akter,et al.  Big data and disaster management: a systematic review and agenda for future research , 2017, Annals of Operations Research.

[189]  Ding-Zhu Du,et al.  Improving Wireless Sensor Network Lifetime through Power Aware Organization , 2005, Wirel. Networks.

[190]  Xiangjian He,et al.  A Sybil attack detection scheme for a forest wildfire monitoring application , 2018, Future Gener. Comput. Syst..

[191]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[192]  Ashutosh Sharma,et al.  An efficient architecture for the accurate detection and monitoring of an event through the sky , 2019, Comput. Commun..

[193]  Forest Strengthening Forest Fire Management in India , 2018 .

[194]  M A Prasetyo,et al.  Review of Landslides Factors at Rinjani Mountain, Lombok Island, West Nusa Tenggara , 2019, IOP Conference Series: Earth and Environmental Science.

[195]  C. Westen,et al.  Remote sensing for natural disaster management , 2000 .

[196]  A.H. Barbat,et al.  Earthquake Risk Scenarios in Urban Areas: A Review with Applications to the Ciutat Vella District in Barcelona, Spain , 2018, International Journal of Architectural Heritage.

[197]  Sartaj Sahni,et al.  An online heuristic for maximum lifetime routing in wireless sensor networks , 2006, IEEE Transactions on Computers.

[198]  Henry Muccini,et al.  Real-time Emergency Response through Performant IoT Architectures , 2019, ISCRAM.

[199]  Chiara Smerzini,et al.  Seismic risk assessment at urban scale from 3D physics-based numerical modeling: the case of Thessaloniki , 2018, Bulletin of Earthquake Engineering.

[200]  Ahmed Mebarki,et al.  Seismic vulnerability assessment at urban scale: Case of Algerian buildings , 2018, International Journal of Disaster Risk Reduction.

[201]  Chao Zhang,et al.  Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System , 2018, Risk analysis : an official publication of the Society for Risk Analysis.

[202]  Lei Shu,et al.  Internet of Things for Disaster Management: State-of-the-Art and Prospects , 2017, IEEE Access.

[203]  M. Walls,et al.  Flood Risk Perceptions and Insurance Choice: Do Decisions in the Floodplain Reflect Overoptimism? , 2018, Risk analysis : an official publication of the Society for Risk Analysis.

[204]  Olivier Berder,et al.  A Hybrid Model for Accurate Energy Analysis of WSN Nodes , 2011, EURASIP J. Embed. Syst..

[205]  Liliana Enciso Quispe,et al.  Improving Lifetime and Availability for Ad Hoc Networks to Emergency and Rescue Scenarios , 2015, WorldCIST.

[206]  Junguo Zhang,et al.  Forest fire detection system based on a ZigBee wireless sensor network , 2008 .

[207]  Feroz Ahmed,et al.  A Self-Configurable Event Coverage Approach for Wireless Sensor Networks , 2019, Int. J. Mob. Comput. Multim. Commun..

[208]  Xu Bing,et al.  Remote sensing and geographic information systems in the spatial temporal dynamics modeling of infectious diseases. , 2006 .

[209]  Sajal K. Das,et al.  Coverage and connectivity issues in wireless sensor networks: A survey , 2008, Pervasive Mob. Comput..

[210]  K. Badarinath,et al.  Forest fire monitoring and burnt area mapping using satellite data: a study over the forest region of Kerala State, India , 2011 .

[211]  U. Kitron,et al.  Spatial analysis of the distribution of Lyme disease in Wisconsin. , 1997, American journal of epidemiology.

[212]  Vijay Nath,et al.  Design of Earthquake Indicator System Using ATmega328p and ADXL335 for Disaster Management , 2019 .

[213]  A Shilpa,et al.  Modern Agriculture Using Wireless Sensor Network (WSN) , 2019 .

[214]  Ahcène Bounceur,et al.  CupCarbon: A New Platform for Designing and Simulating Smart-City and IoT Wireless Sensor Networks (SCI-WSN) , 2016, ICC 2016.

[215]  Majid Bagheri,et al.  Wireless Sensor Networks for Early Detection of Forest Fires , 2007, 2007 IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems.

[216]  Maurizio Tesconi,et al.  CrisMap: a Big Data Crisis Mapping System Based on Damage Detection and Geoparsing , 2018, Information Systems Frontiers.

[217]  Young Wook Cho,et al.  The study on how to remove the shadow area for WSN based indoor disaster monitoring system , 2017, Cluster Computing.

[218]  Qiang Xu,et al.  RADAR REMOTE SENSING APPLICATIONS IN LANDSLIDE MONITORING WITH MULTI-PLATFORM INSAR OBSERVATIONS: A CASE STUDY FROM CHINA , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[219]  Farrokh Nadim,et al.  Stochastic design of an early warning system , 2008 .

[220]  Silvester Tena,et al.  Wireless Sensor Network for Landslide Monitoring in Nusa Tenggara Timur , 2011 .

[221]  Ashok Kote,et al.  Security Issues in Geo-Spatial Big Data Analytics with Special Reference to Disaster Management , 2018, Springer Series in Geomechanics and Geoengineering.

[222]  Xinyue Ye,et al.  Social media analytics for natural disaster management , 2018, Int. J. Geogr. Inf. Sci..

[223]  Deborah Estrin,et al.  Geography-informed energy conservation for Ad Hoc routing , 2001, MobiCom '01.

[224]  Yong Zhang,et al.  An incident information management framework based on data integration, data mining, and multi-criteria decision making , 2011, Decis. Support Syst..

[225]  Terje Gobakken,et al.  Inventory of Small Forest Areas Using an Unmanned Aerial System , 2015, Remote. Sens..

[226]  D R Roberts,et al.  Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. , 1994, The American journal of tropical medicine and hygiene.

[227]  Loïc Lagadec,et al.  CupCarbon-Lab: An IoT emulator , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[228]  Aníbal Ollero,et al.  Multiple eyes in the skies: architecture and perception issues in the COMETS unmanned air vehicles project , 2005, IEEE Robotics & Automation Magazine.

[229]  Biswajeet Pradhan,et al.  Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree , 2016, Landslides.

[230]  Marco Barla,et al.  An integrated methodology for landslides’ early warning systems , 2016, Landslides.

[231]  Majdi Mansouri,et al.  Crisis management using MAS-based wireless sensor networks , 2013, Comput. Networks.

[232]  Roopam Gupta,et al.  Route-Discovery Optimization in LAR: A Review , 2011, SocProS.

[233]  V. Murray,et al.  The Sendai Framework for Disaster Risk Reduction: Renewing the Global Commitment to People’s Resilience, Health, and Well-being , 2015, International Journal of Disaster Risk Science.

[234]  Olivier Berder,et al.  TAD-MAC: Traffic-Aware Dynamic MAC Protocol for Wireless Body Area Sensor Networks , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[235]  Ammar Belatreche,et al.  Challenges for large-scale implementations of spiking neural networks on FPGAs , 2007, Neurocomputing.

[236]  Ou Ma,et al.  Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization , 2019, Drones.

[237]  Dieu Tien Bui,et al.  Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS , 2017 .

[238]  Mohammad Abdul Awal,et al.  DUMBONET: a multimedia communication system for collaborative emergency response operations in disaster-affected areas , 2007 .

[239]  Junghwan Kim,et al.  The Effectiveness of a Wireless Sensor Network System for Landslide Monitoring , 2020, IEEE Access.