Invited perspectives: How machine learning will change flood risk and impact assessment
暂无分享,去创建一个
Dennis Wagenaar | David Lallemant | Laddaporn Ruangpan | Emir Hartato | Alex Curran | Mariano Balbi | Alok Bhardwaj | Robert Soden | Gizem Mestav Sarica | G. Molinario
[1] Stephen Jarvis,et al. Predicting floods with Flickr tags , 2017, PloS one.
[2] L. Gnanappazham,et al. Comparison of Urban Growth Modeling Using Deep Belief and Neural Network Based Cellular Automata Model—A Case Study of Chennai Metropolitan Area, Tamil Nadu, India , 2019, Journal of Geographic Information System.
[3] Kwok-wing Chau,et al. Flood Prediction Using Machine Learning Models: Literature Review , 2018, Water.
[4] Bruno Merz,et al. Multi-variate flood damage assessment: a tree-based data-mining approach , 2013 .
[5] Laura Giurca Vasilescu,et al. Disaster Management CYCLE – a theoretical approach , 2008 .
[6] Wei You,et al. Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine , 2016, Remote. Sens..
[7] C. Cao,et al. A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm , 2015 .
[8] Bruno Merz,et al. How useful are complex flood damage models? , 2014 .
[9] Animesh K. Gain,et al. Multi-Variate Analyses of Flood Loss in Can Tho City, Mekong Delta , 2015 .
[10] Frieder. Andreas Perthes. Carolus Fridericus Gauss Theoria motus corporum coelestium in sectionibus conicis solem ambientium , 1855 .
[11] G. Mountrakis,et al. Urban Growth Prediction: A Review of Computational Models and Human Perceptions , 2012 .
[12] Leonardo Alfonso,et al. Can assimilation of crowdsourced data in hydrological modelling improve flood prediction , 2017 .
[13] Henry V. Burton,et al. Replicating the Recovery following the 2014 South Napa Earthquake using Stochastic Process Models , 2018, Earthquake Spectra.
[14] Mahmoud Reza Delavar,et al. Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm , 2016, Sensors.
[15] Marcello Restelli,et al. Tree‐based reinforcement learning for optimal water reservoir operation , 2010 .
[16] R. Muir-Wood,et al. Flood risk and climate change: global and regional perspectives , 2014 .
[17] B. Merz,et al. Development of FLEMOcs – a new model for the estimation of flood losses in the commercial sector , 2010 .
[18] J. Townshend,et al. Characterizing the magnitude, timing and duration of urban growth from time series of Landsat-based estimates of impervious cover , 2016 .
[19] Pieter van Gelder,et al. Computational intelligence methods for the efficient reliability analysis of complex flood defence structures , 2011 .
[20] Scott B. Miles,et al. Integrating Performance-Based Engineering and Urban Simulation to Model Post-Earthquake Housing Recovery , 2018, Earthquake Spectra.
[21] Heidi Kreibich,et al. Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy) , 2017, Natural Hazards and Earth System Sciences.
[22] V. Klemas,et al. Remote Sensing of Floods and Flood-Prone Areas: An Overview , 2015 .
[23] Ioana Popescu,et al. Citizen observations contributing to flood modelling: opportunities and challenges , 2017 .
[24] S. Noble. Algorithms of Oppression: How Search Engines Reinforce Racism , 2018 .
[25] S. Jonkman,et al. Developments in the management of flood defences and hydraulic infrastructure in the Netherlands , 2018 .
[26] Cheryl A. Palm,et al. Socioecologically informed use of remote sensing data to predict rural household poverty , 2019, Proceedings of the National Academy of Sciences.
[27] A. C. Neves,et al. Structural health monitoring of bridges: a model-free ANN-based approach to damage detection , 2017, Journal of Civil Structural Health Monitoring.
[28] B. Merz,et al. Development and evaluation of FLEMOps - a new Flood Loss Estimation MOdel for the private sector , 2008 .
[29] Asaad Y. Shamseldin,et al. Application of surrogate artificial intelligent models for real‐time flood routing , 2013 .
[30] Fernando Nardi,et al. Integrating VGI and 2D hydraulic models into a data assimilation framework for real time flood forecasting and mapping , 2019, Geo spatial Inf. Sci..
[31] Bruno Merz,et al. Review article "Assessment of economic flood damage" , 2010 .
[32] K. M. de Bruijn,et al. Uncertainty in flood damage estimates and its potential effect on investment decisions , 2015 .
[33] J.A.E. Ten Veldhuis,et al. Decision-tree analysis of factors influencing rainfall-related building structure and content damage , 2014 .
[34] Valeria V. Krzhizhanovskaya,et al. Machine learning methods for environmental monitoring and flood protection , 2011 .
[35] Dimitri P. Solomatine,et al. River flow forecasting using artificial neural networks , 2001 .
[36] P. D. Heermann,et al. Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..
[37] Peter Kontschieder,et al. The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[38] Robert C. Balling,et al. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover , 2018 .
[39] Luke J. Prendergast,et al. Structural Health Monitoring for Performance Assessment of Bridges under Flooding and Seismic Actions , 2018, Structural Engineering International.
[40] Robert J. Abrahart,et al. Neural network modelling of non-linear hydrological relationships , 2007 .
[41] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[42] Fabio Roli,et al. Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..
[43] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[44] Suzana Dragicevic,et al. Machine Learning Techniques for Modelling Short Term Land-Use Change , 2017, ISPRS Int. J. Geo Inf..
[45] A. Keane,et al. Evolutionary Optimization of Computationally Expensive Problems via Surrogate Modeling , 2003 .
[46] Jeroen C. J. H. Aerts,et al. Accounting for risk aversion, income distribution and social welfare in cost‐benefit analysis for flood risk management , 2017 .
[47] Aydan Menderes,et al. Automatic Detection of Damaged Buildings after Earthquake Hazard by Using Remote Sensing and Information Technologies , 2015 .
[48] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[49] W.P.S. Dias,et al. Evaluating adaptation measures for reducing flood risk: A case study in the city of Colombo, Sri Lanka , 2019, International Journal of Disaster Risk Reduction.
[50] João Cardoso,et al. Review and application of Artificial Neural Networks models in reliability analysis of steel structures , 2015 .
[51] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[52] Emma M. Hill,et al. Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew , 2019, Remote. Sens..
[53] S. Jonkman. Global Perspectives on Loss of Human Life Caused by Floods , 2005 .
[54] Anna Rita Scorzini,et al. Testing empirical and synthetic flood damage models: the case of Italy , 2019, Natural Hazards and Earth System Sciences.
[55] Arnejan van Loenen,et al. Harvesting Social Media for Generation of Near Real-time Flood Maps☆ , 2016 .
[56] Andreas Dengel,et al. Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks , 2017, 2019 IEEE International Conference on Image Processing (ICIP).
[57] Bruno Merz,et al. Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT‐FLEMO , 2017, Risk analysis : an official publication of the Society for Risk Analysis.
[58] Mike Kama,et al. Predictive risk mapping of an environmentally-driven infectious disease using spatial Bayesian networks: A case study of leptospirosis in Fiji , 2018, PLoS neglected tropical diseases.
[59] Molly E. Brown,et al. Environmental variability and child growth in Nepal. , 2015, Health & place.
[60] Virginia E. Eubanks. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor , 2018 .
[61] Dimitri Solomatine,et al. Comparative analysis of conceptual models with error correction, artificial neural networks and committee models , 2014 .
[62] Dennis Wagenaar,et al. Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction , 2020, Risk analysis : an official publication of the Society for Risk Analysis.
[63] Joost van de Weijer,et al. Multi-modal Deep Learning Approach for Flood Detection , 2017, MediaEval.
[64] T. Schweckendiek,et al. Value of Information of Structural Health Monitoring in Asset Management of Flood Defences , 2019, Infrastructures.
[65] Morgan G. Ames. Deconstructing the algorithmic sublime , 2018, Big Data Soc..
[66] Jeroen C. J. H. Aerts,et al. Regional disaster impact analysis: comparing Input-Output and Computable General Equilibrium models , 2015 .
[67] Heidi Kreibich,et al. Social media as an information source for rapid flood inundation mapping , 2015 .
[68] Robert Soden,et al. Infrastructuring the Imaginary: How Sea-Level Rise Comes to Matter in the San Francisco Bay Area , 2019, CHI.
[69] Avi Ostfeld,et al. Data-driven modelling: some past experiences and new approaches , 2008 .
[70] Li-Chiu Chang,et al. Building ANN-Based Regional Multi-Step-Ahead Flood Inundation Forecast Models , 2018, Water.
[71] B. Modu,et al. Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System , 2017 .
[72] Giovanni Coco,et al. Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata , 2019, Int. J. Geogr. Inf. Sci..
[73] A. Thieken,et al. Adaptability and transferability of flood loss functions in residential areas , 2013 .
[74] Dimitri P. Solomatine,et al. Machine learning in real-time control of water systems , 2002 .
[75] B. Ligon. Infectious diseases that pose specific challenges after natural disasters: a review. , 2006, Seminars in pediatric infectious diseases.
[76] Dennis Wagenaar,et al. Multi-variable flood damage modelling with limited data using supervised learning approaches , 2017 .
[77] Florian Pappenberger,et al. Action-based flood forecasting for triggering humanitarian action , 2016 .
[78] Peter M. A. Sloot,et al. Time-Frequency Methods for Structural Health Monitoring , 2014, Sensors.
[79] Os Keyes,et al. The Misgendering Machines , 2018, Proc. ACM Hum. Comput. Interact..
[80] Stefan Lüdtke,et al. Flood loss estimation using 3D city models and remote sensing data , 2018, Environ. Model. Softw..
[81] A.C.W.M. Vrouwenvelder,et al. Reliability analysis of flood defence systems , 2004 .
[82] Wei Lee Woon,et al. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks , 2017 .
[83] E. Ryan,et al. Infectious diseases of severe weather-related and flood-related natural disasters , 2006, Current opinion in infectious diseases.
[84] Stefan Lüdtke,et al. Regional and Temporal Transferability of Multivariable Flood Damage Models , 2018 .
[85] M. Kok,et al. Large Scale Flood Hazard Analysis by Including Defence Failures on the Dutch River System , 2019, Water.
[86] A. Soldati,et al. Artificial neural network approach to flood forecasting in the River Arno , 2003 .
[87] Kozo Watanabe,et al. Machine learning methods reveal the temporal pattern of dengue incidence using meteorological factors in metropolitan Manila, Philippines , 2018, BMC Infectious Diseases.