Flood Hazard Risk Mapping Using a Pseudo Supervised Random Forest
暂无分享,去创建一个
Morteza Esfandiari | Ghasem Abdi | Shabnam Jabari | Heather McGrath | David Coleman | H. McGrath | S. Jabari | G. Abdi | M. Esfandiari | D. Coleman
[1] R. Amer,et al. Propensity for erosion and deposition in a deltaic wetland complex: Implications for river management and coastal restoration , 2017 .
[2] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[3] H. McGrath,et al. FLOOD MAPPING USING RANDOM FOREST AND IDENTIFYING THE ESSENTIAL CONDITIONING FACTORS; A CASE STUDY IN FREDERICTON, NEW BRUNSWICK, CANADA , 2020 .
[4] Yun Zhang,et al. Improving UAV imaging quality by optical sensor fusion: an initial study , 2017 .
[5] R. Barber,et al. TELEMAC: An efficient hydrodynamics suite for massively parallel architectures , 2011 .
[6] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[7] Mustafa Neamah Jebur,et al. Flood susceptibility mapping using integrated bivariate and multivariate statistical models , 2014, Environmental Earth Sciences.
[8] L. Smith. Satellite remote sensing of river inundation area, stage, and discharge: a review , 1997 .
[9] Mahyat Shafapour Tehrany,et al. Flood susceptibility assessment using GIS-based support vector machine model with different kernel types , 2015 .
[10] Assessing flash flood hazard in an arid mountainous region , 2013, Arabian Journal of Geosciences.
[11] Anthony J. Jakeman,et al. Flood inundation modelling: A review of methods, recent advances and uncertainty analysis , 2017, Environ. Model. Softw..
[12] Vladan Babovic,et al. GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING 1 , 2001 .
[13] Shaowen Wang,et al. A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping , 2018, JAWRA Journal of the American Water Resources Association.
[14] C. Rennó,et al. Height Above the Nearest Drainage – a hydrologically relevant new terrain model , 2011 .
[15] Yun Zhang,et al. Adaptive band selection for pan-sharpening of hyperspectral images , 2020 .
[16] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[17] D. Scott,et al. How Flood Experience and Risk Perception Influences Protective Actions and Behaviours among Canadian Homeowners , 2018, Environmental Management.
[18] Marcos Rodrigo Momo,et al. HAND contour: a new proxy predictor of inundation extent , 2016 .
[19] J. Monaghan. Simulating Free Surface Flows with SPH , 1994 .
[20] Miroslav Nastev,et al. A comparison of simplified conceptual models for rapid web-based flood inundation mapping , 2018, Natural Hazards.
[21] Xavier-Andoni Tibau,et al. Why Cohen’s Kappa should be avoided as performance measure in classification , 2019, PloS one.
[22] B. Matthews. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.
[23] Yun Zhang,et al. Understanding image fusion , 2004 .
[24] Gustavo Andrei Speckhann,et al. Flood hazard mapping in Southern Brazil: a combination of flow frequency analysis and the HAND model , 2018 .
[25] Mustafa Neamah Jebur,et al. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method , 2015, Stochastic Environmental Research and Risk Assessment.
[26] Q. Schiermeier. Increased flood risk linked to global warming , 2011, Nature.
[27] H. Pourghasemi,et al. Identification of Critical Flood Prone Areas in Data-Scarce and Ungauged Regions: A Comparison of Three Data Mining Models , 2017, Water Resources Management.
[28] Paul W. Cleary,et al. Modelling the impact of dam failure scenarios on flood inundation using SPH , 2014 .
[29] Zongxue Xu,et al. Mapping flood susceptibility in mountainous areas on a national scale in China. , 2018, The Science of the total environment.
[30] M. McHugh. Interrater reliability: the kappa statistic , 2012, Biochemia medica.
[31] H. Pourghasemi,et al. A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique , 2016, Natural Hazards.
[32] Hyung-Sup Jung,et al. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea , 2017 .
[33] P. Bates,et al. Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models , 2009 .
[34] Mateeul Haq,et al. Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan , 2012 .
[35] Slobodan P. Simonovic,et al. Future Changes in Flood Hazards across Canada under a Changing Climate , 2018, Water.
[36] J. V. Soares,et al. HAND, a new terrain descriptor using SRTM-DEM: Mapping terra-firme rainforest environments in Amazonia , 2008 .
[37] Mustafa Neamah Jebur,et al. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS , 2013 .
[38] A. R. Mahmud,et al. An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia , 2012, Environmental Earth Sciences.
[39] Zongxue Xu,et al. Assessment of urban flood susceptibility using semi-supervised machine learning model. , 2019, The Science of the total environment.
[40] Nikolas Prechtel,et al. An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image , 2014, Comput. Geosci..
[41] H. Pourghasemi,et al. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran , 2016 .
[42] Simon D. Jones,et al. Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques , 2019, CATENA.
[43] H. Pourghasemi,et al. Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran , 2016 .
[44] D. Tarboton. A new method for the determination of flow directions and upslope areas in grid digital elevation models , 1997 .
[45] E. Toth,et al. Comparison of short-term rainfall prediction models for real-time flood forecasting , 2000 .
[46] Mustafa Neamah Jebur,et al. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .
[47] Jian Ye,et al. Simulation of Curved Open Channel Flows by 3D Hydrodynamic Model , 1998 .
[48] B. Rogers,et al. SPH Modeling of Shallow Flow with Open Boundaries for Practical Flood Simulation , 2012 .