The role of susceptibility, exposure and vulnerability as drivers of flood disaster risk at the parish level

[1]  G. di Baldassarre,et al.  Floodplains in the Anthropocene: A Global Analysis of the Interplay Between Human Population, Built Environment, and Flood Severity , 2020, Water Resources Research.

[2]  Mohammad Ali Ghorbani,et al.  GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia , 2020 .

[3]  Biswajeet Pradhan,et al.  Flash flood susceptibility modelling using functional tree and hybrid ensemble techniques , 2020, Journal of Hydrology.

[4]  Quoc Bao Pham,et al.  Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning , 2020 .

[5]  S. Oliveira,et al.  A comprehensive approach to understanding flood risk drivers at the municipal level. , 2020, Journal of environmental management.

[6]  Sani Isah Abba,et al.  Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques , 2019, Remote. Sens..

[7]  Dhekra Souissi,et al.  GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia , 2020, Geocarto International.

[8]  Kel Markert,et al.  Operational Flood Risk Index Mapping for Disaster Risk Reduction Using Earth Observations and Cloud Computing Technologies: A Case Study on Myanmar , 2019, Front. Environ. Sci..

[9]  Pedro Santos,et al.  Social vulnerability and local level assessments: a new approach for planning , 2019 .

[10]  Biswajeet Pradhan,et al.  Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. , 2019, The Science of the total environment.

[11]  Quoc Bao Pham,et al.  Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models. , 2019, The Science of the total environment.

[12]  Wei Chen,et al.  Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. , 2019, Journal of environmental management.

[13]  Pijush Samui,et al.  A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area. , 2019, The Science of the total environment.

[14]  B. Pradhan,et al.  Natural Hazards and Social Vulnerability of Place: The Strength-Based Approach Applied to Wollongong, Australia , 2019, International Journal of Disaster Risk Science.

[15]  Hamid Reza Pourghasemi,et al.  Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model , 2019, Remote. Sens..

[16]  Global Assessment Report on Disaster Risk Reduction 2019 , 2019, Global Assessment Report on Disaster Risk Reduction (GAR).

[17]  Paraskevas Tsangaratos,et al.  Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. , 2019, The Science of the total environment.

[18]  B. Pradhan,et al.  A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods , 2019, Journal of Hydrology.

[19]  E. Reis,et al.  A flood susceptibility model at the national scale based on multicriteria analysis. , 2019, The Science of the total environment.

[20]  J. Adamowski,et al.  An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. , 2019, The Science of the total environment.

[21]  J. L. Barros,et al.  Decennial comparison of changes in social vulnerability: A municipal analysis in support of risk management , 2018, International Journal of Disaster Risk Reduction.

[22]  Andreas Paul Zischg,et al.  Flood risk (d)evolution: Disentangling key drivers of flood risk change with a retro-model experiment. , 2018, The Science of the total environment.

[23]  Omid Rahmati,et al.  Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models , 2018 .

[24]  Abhishek Ghosh,et al.  Application of analytical hierarchy process (AHP) for flood risk assessment: a case study in Malda district of West Bengal, India , 2018, Natural Hazards.

[25]  A. Zhu,et al.  Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China. , 2018, The Science of the total environment.

[26]  B. Choubin,et al.  Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches , 2018, Environmental Earth Sciences.

[27]  Shanzhen Yi,et al.  Assessment of flood susceptible areas using spatially explicit, probabilistic multi-criteria decision analysis , 2018 .

[28]  Zongxue Xu,et al.  Mapping flood susceptibility in mountainous areas on a national scale in China. , 2018, The Science of the total environment.

[29]  H. Pourghasemi,et al.  Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. , 2018, The Science of the total environment.

[30]  Hamid Reza Pourghasemi,et al.  Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India , 2018, Modeling Earth Systems and Environment.

[31]  E. Reis,et al.  Assessment of stream flood susceptibility: a cross‐analysis between model results and flood losses , 2018 .

[32]  J. Santos,et al.  Damaging flood severity assessment in Northern Portugal over more than 150 years (1865–2016) , 2018, Natural Hazards.

[33]  Sittimont Kanjanabootra,et al.  Assessing flood hazard using flood marks and analytic hierarchy process approach: a case study for the 2013 flood event in Quang Nam, Vietnam , 2018, Natural Hazards.

[34]  Kris A. Johnson,et al.  Estimates of present and future flood risk in the conterminous United States , 2017 .

[35]  Dieu Tien Bui,et al.  A novel hybrid artificial intelligence approach for flood susceptibility assessment , 2017, Environ. Model. Softw..

[36]  Peter Salamon,et al.  An operational procedure for rapid flood risk assessment in Europe , 2017 .

[37]  M. Diakakis,et al.  Comparing flood mortality in Portugal and Greece (Western and Eastern Mediterranean) , 2017 .

[38]  Hyung-Sup Jung,et al.  Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea , 2017 .

[39]  H. Pourghasemi,et al.  Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models , 2016, Environmental Monitoring and Assessment.

[40]  C. Cao,et al.  Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas , 2016 .

[41]  B. Pradhan,et al.  Analysis on causes of flash flood in Jeddah city (Kingdom of Saudi Arabia) of 2009 and 2011 using multi-sensor remote sensing data and GIS , 2016 .

[42]  A. Hilberts,et al.  Large-scale and High-resolution Flood Risk Model for Japan , 2016 .

[43]  J. Santos,et al.  Historical damaging flood records for 1871–2011 in Northern Portugal and underlying atmospheric forcings , 2015 .

[44]  P. Bates,et al.  Usefulness and limitations of global flood risk models , 2015 .

[45]  B. Ripley.  Classification and Regression Trees , 2015 .

[46]  Margarida L. R. Liberato,et al.  Daily Precipitation Extreme Events in the Iberian Peninsula and Its Association with Atmospheric Rivers , 2015 .

[47]  W. Botzen,et al.  Combining hazard, exposure and social vulnerability to provide lessons for flood risk management , 2015 .

[48]  R. Trigo,et al.  Circulation weather types and spatial variability of daily precipitation in the Iberian Peninsula , 2014, Front. Earth Sci..

[49]  Mustafa Neamah Jebur,et al.  Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS , 2014 .

[50]  R. Trigo,et al.  DISASTER: a GIS database on hydro-geomorphologic disasters in Portugal , 2014, Natural Hazards.

[51]  Peijun Shi,et al.  Measuring social vulnerability to natural hazards in the Yangtze River Delta region, China , 2013, International Journal of Disaster Risk Science.

[52]  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 .

[53]  Brenden Jongman,et al.  Assessing flood risk at the global scale: model setup, results, and sensitivity , 2013 .

[54]  Jidong Wu,et al.  Comprehensive assessment of flood risk using the classification and regression tree method , 2013, Stochastic Environmental Research and Risk Assessment.

[55]  De Groeve Tom,et al.  Recording Disaster Losses: Recommendations for a European approach , 2013 .

[56]  Á. Felicísimo,et al.  Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study , 2013, Landslides.

[57]  Shaohong Wu,et al.  Flood loss analysis and quantitative risk assessment in China , 2012, Natural Hazards.

[58]  Dong Keun Yoon,et al.  Assessment of social vulnerability to natural disasters: a comparative study , 2012, Natural Hazards.

[59]  Wolfgang Kron,et al.  How to deal properly with a natural catastrophe database – analysis of flood losses , 2012 .

[60]  R. Trigo,et al.  Trends in frequency indices of daily precipitation over the Iberian Peninsula during the last century , 2011 .

[61]  José Manuel Mendes,et al.  Social Vulnerability Indexes as Planning Tools: Beyond the Preparedness Paradigm , 2009 .

[62]  Amir Etemad-Shahidi,et al.  An alternative approach for the prediction of significant wave heights based on classification and regression trees , 2008 .

[63]  A. Prasad,et al.  Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction , 2006, Ecosystems.

[64]  K. Forbes.  2 20 th Century Portuguese Climate and Climate Scenarios , 2005 .

[65]  Roman Timofeev,et al.  Classification and Regression Trees(CART)Theory and Applications , 2004 .

[66]  W. L. Shirley,et al.  Social Vulnerability to Environmental Hazards , 2003 .

[67]  F. D. Santos,et al.  Climate change in portugal scenarios, impacts and adaptation measures : Siam project , 2002 .

[68]  C. Mclay,et al.  Predicting groundwater nitrate concentrations in a region of mixed agricultural land use: a comparison of three approaches. , 2001, Environmental pollution.

[69]  R. Trigo,et al.  Circulation weather types and their influence on the precipitation regime in Portugal. , 2000 .

[70]  Ezio Todini,et al.  An operational decision support system for flood risk mapping, forecasting and management , 1999 .

[71]  R. E. Miles,et al.  Designing strategic human resources systems , 1984 .