scholarly journals Application of Fuzzy TOPSIS to Flood Hazard Mapping for Levee Failure

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 592 ◽  
Author(s):  
Tae Hyung Kim ◽  
Byunghyun Kim ◽  
Kun-Yeun Han
Keyword(s):  
Flood Hazard ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Topsis ◽  
Dam Failure ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Hazard Maps ◽  
Levee Failure ◽  
Flood Hazard Map ◽  
Flood Hazard Maps

This paper proposes a new approach to consider the uncertainties for constructing flood hazard maps for levee failure. The flood depth, velocity, and arrival time were estimated by the 2-Dimensional model and were considered as flood indices for flood hazard mapping. Each flood index predicted from the 2-D flood analysis based on several scenarios was fuzzified to reflect the uncertainties of the indices. The fuzzified flood indices were integrated using the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), resulting in a single graded flood hazard map. This methodology was applied to the Gam river in South Korea and confirmed that the Fuzzy MCDM (Multiple Criteria Decision Making) technique can be used to produce flood hazard maps. The flood hazard map produced in this study compared with the current flood hazard map of MOLIT (Ministry of Land, Infrastructure and Transports). This study found that the proposed methodology was more advantageous than the current methods with regard to the accuracy and grading of the flood areas, as well as in regard to an integrated single map. This report is expected to be expand upon other floods, including dam failure and urban flooding.

Probabilistic Flood Hazard Maps at Ungauged Locations Using Multivariate Extreme Values Approach

2020 ◽  
Author(s):  
Kiran Kezhkepurath Gangadhara ◽  
Srinivas Venkata Vemavarapu
Keyword(s):  
Flood Risk ◽  
Flood Hazard ◽  
Flood Plain ◽  
Hydrologic Model ◽  
Regional Frequency Analysis ◽  
Hazard Map ◽  
Hazard Maps ◽  
Flood Hydrograph ◽  
Flood Hazard Map ◽  
Flood Hazard Maps

<p>Flood hazard maps are essential for development and assessment of flood risk management strategies. Conventionally, flood hazard assessment is based on deterministic approach which involves deriving inundation maps considering hydrologic and hydraulic models. A flood hydrograph corresponding to a specified return period is derived using a hydrologic model, which is then routed through flood plain of the study area to estimate water surface elevations and inundation extent with the aid of a hydraulic model. A more informative way of representing flood risk is through probabilistic hazard maps, which additionally provide information on the uncertainty associated with the extent of inundation. To arrive at a probabilistic flood hazard map, several flood hydrographs are generated, representing possible scenarios for flood events over a long period of time (e.g., 500 to 1000 years). Each of those hydrographs is routed through the flood plain and probability of inundation for all locations in the plain is estimated to derive the probabilistic flood hazard map. For gauged catchments, historical streamflow and/or rainfall data may be used to determine design flood hydrographs and the corresponding hazard maps using various strategies. In the case of ungauged catchments, however, there is a dearth of procedures for prediction of flood hazard maps. To address this, a novel multivariate regional frequency analysis (MRFA) approach is proposed. It involves (i) use of a newly proposed clustering methodology for regionalization of catchments, which accounts for uncertainty arising from ambiguity in choice of various potential clustering algorithms (which differ in underlying clustering strategies) and their initialization, (ii) fitting of a multivariate extremes model to information pooled from catchments in homogeneous region to generate synthetic flood hydrographs at ungauged target location(s), and (iii) routing of the hydrographs through the flood plain using LISFLOOD-FP model to derive probabilistic flood hazard map. The MRFA approach is designed to predict flood hydrograph related characteristics (peak flow, volume and duration of flood) at target locations in ungauged basins by considering watershed related characteristics as predictor/explanatory variables. An advantage of the proposed approach is its ability to account for uncertainty in catchment regionalization and dependency between all the flood hydrograph related characteristics reliably. Thus, the synthetic flood hydrographs generated in river basins appear more realistic depicting the observed dependence structure among flood hydrograph characteristics. The approach alleviates several uncertainties found in conventional methods (based on conceptual, probabilistic or geomorphological approaches) which affect estimation of flood hazard. Potential of the proposed approach is demonstrated through a case study on catchments in Mahanadi river basin of India, which extends over 141,600 km<sup>2</sup> and is frequently prone to floods. Comparison is shown between flood hazard map obtained based on true at-site data and that derived based on the proposed MRFA approach by considering the respective sites to be pseudo-ungauged. Coefficient of correlation and root mean squared error considered for performance evaluation indicated that the proposed approach is promising.</p>

Global flood hazard map and exposed GDP inter-comparison for China

2020 ◽  
Author(s):  
Philip Ward ◽  
Jerom Aerts ◽  
Steffi Uhlemann-Elmer ◽  
Dirk Eilander
Keyword(s):  
Flood Hazard ◽  
Insurance Industry ◽  
Hazard Map ◽  
Hazard Maps ◽  
High Concentration ◽  
Flood Modelling ◽  
Flood Hazard Map ◽  
River Training ◽  
Flood Hazard Maps

<p>In this contribution we carried out a comprehensive comparison of flood hazard maps of 8 global flood models for the country of China based on a collective effort of the flood modelling community to participate in this study. In doing so, we assessed how differences in the simulated flood extent between the models lead to differences in simulated exposed GDP and expected annual exposed GDP. This is carried out by addressing the variation in different model structures and the variability between flood hazard maps. Our comparison uses both publicly available GFMs (GLOFRIS, ECMWF, CAMA-UT, JRC, and CIMA-UNEP) as well as industry models (Fathom, KatRisk, and JBA Risk Management) that are applied within the wider re-insurance industry. We expand upon the existing work of global flood model inter-comparison studies (e.g. Trigg et al., 2016; Bernhoffen et al., 2018) by including industry models, the pluvial flood component, and the effects of standards of protection on the flood hazard and exposure.</p><p>China is selected as our case study area because it poses many challenges to flood modelling: data scarcity; a variety of flood mechanisms spanning many climatic zones; complex topography; strong anthropogenic influence on the flood regimes, for example through river training; and a very high concentration of exposure. Moreover, China is prone to severe flood events. For example, in June 2016 alone more than 60 million people were affected by floods, resulting in an estimated damage of $22 bn (CRED 2016).</p><p>This abstract is an adaptation of “Global flood hazard map and exposed GDP comparison: a China case study” (Aerts et al., under review) and parts of this work have been presented at EGU 2019.</p>

Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment 

2021 ◽  
Author(s):  
Andrea Magnini ◽  
Michele Lombardi ◽  
Simone Persiano ◽  
Antonio Tirri ◽  
Francesco Lo Conti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hazard Assessment ◽  
Water Depth ◽  
Flood Hazard ◽  
Research Centre ◽  
Hazard Mapping ◽  
Hazard Maps ◽  
Flood Hazard Assessment ◽  
Flood Prone Areas ◽  
Flood Hazard Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i.e.</span></span><span xml:lang="EN-US" data-contrast="auto"><span> land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-</span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein “DEM-based methods”) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km</span></span><sup><span xml:lang="EN-US" data-contrast="auto"><span>2</span></span></sup><span xml:lang="EN-US" data-contrast="auto"><span>, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (</span></span><span xml:lang="EN-US" data-contrast="auto"><span>i</span></span><span xml:lang="EN-US" data-contrast="auto"><span>) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.</span></span><span> </span></p>

scholarly journals Going beyond the Flood Insurance Rate Map: insights from flood hazard map co-production

2017 ◽  
Author(s):  
Adam Luke ◽  
Brett F. Sanders ◽  
Kristen Goodrich ◽  
David L. Feldman ◽  
Danielle Boudreau ◽  
...  
Keyword(s):  
Flood Hazard ◽  
End Users ◽  
User Preferences ◽  
Flood Insurance ◽  
Hazard Map ◽  
Flood Hazards ◽  
Hazard Maps ◽  
Baseline Hazard ◽  
Flood Maps ◽  
Flood Hazard Maps

Abstract. Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end-users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end-users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River Valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end-user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end-users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included: (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies.

scholarly journals Flood Hazard Assessment Using AHP in Corum, Turkey

2021 ◽  
Vol 12 (2) ◽  
pp. 01-26
Author(s):  
Derya Ozturk ◽  
◽  
Ilknur Yilmaz ◽  
Ufuk Kirbas ◽  
◽  
...  
Keyword(s):  
Hazard Assessment ◽  
Flood Hazard ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Analytic Hierarchy ◽  
Flood Hazard Map ◽  
Structural Measures ◽  
High Flood ◽  
Hierarchy Process ◽  
Very High

In this study, the flood hazard of Corum province (Turkey) was investigated using the Analytic Hierarchy Process (AHP), which is one of the most popular Multi-criteria Decision Analysis (MCDA) methods, based on Geographic Information System (GIS). As a result of the AHP process, Corum province was categorized into five flood hazard classes: very high, high, medium, low, and very low. It was determined that 3% of the total area is under a very high flood hazard, and 25% is considered a high flood hazard. To assess the validity of the flood hazard map, the results were compared with the historical flood inventory. Our hazard map was compatible with the historical flood inventory, and our hazard map can now be used to estimate the areas that are threatened by possible floods. When the existing structural measures are overlapped with the hazard map in Corum, it is understood that a large part of the structural measures carried out to date have focused on the areas of very high and high flood hazard in the flood hazard map. Future structural measures and detailed studies should now address other areas identified as under threat in the flood hazard map. Our results suggest that the hazard assessment based on MCDA is suitable for flood hazard mapping.

scholarly journals Flood Hazard Mapping Using Fuzzy Logic, Analytical Hierarchy Process, and Multi-Source Geospatial Datasets

2021 ◽  
Vol 13 (23) ◽  
pp. 4761
Author(s):  
Saeid Parsian ◽  
Meisam Amani ◽  
Armin Moghimi ◽  
Arsalan Ghorbanian ◽  
Sahel Mahdavi
Keyword(s):  
Remote Sensing ◽  
Analytical Hierarchy Process ◽  
Vegetation Index ◽  
Flood Hazard ◽  
Fuzzy Membership Function ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Topographic Wetness Index ◽  
Flood Hazard Map ◽  
Hierarchy Process

Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood hazard map with five classes of susceptible zones. The bi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) images, acquired before and on the day of the flood event, were used to evaluate the accuracy of the produced flood hazard map. The results indicated that 95.16% of the actual flooded areas were classified as very high and high flood hazard classes, demonstrating the high potential of this approach for flood hazard mapping.

scholarly journals FLOOD HAZARD MAPPING AT LONG XUYEN QUADRANGLE IN 2015 USING GEOGRAPHIC INFORMATION AND REMOTE SENSING

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 275-281
Author(s):  
N. T. H. Diep ◽  
T. H. Duy ◽  
P. K. Diem ◽  
N. T. B. Nam ◽  
N. T. T. Huong
Keyword(s):  
Flood Hazard ◽  
Mekong Delta ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Water Index ◽  
Flood Hazard Map ◽  
Severe Flood ◽  
Normalized Difference Water Index ◽  
Spss Software ◽  
Sentinel 2

<p><strong>Abstract.</strong> In recent year, the flooding has been occurred with higher frequency at Long Xuyen Quadrangle areas of Mekong Delta, Vietnam. It was considered as a major natural disaster which effects on the physical and spiritual in people’s life in this area. This research aims to generate a flood hazard map and assess the flood situation at Long Xuyen quadrangle in 2015. The MNDWI (Modification of Normalized Difference Water Index) extracting from Sentinel 2 image was used to map the flood extent at Long Xuyen quadrangle during rainy season in 2015. The statistics method was estimated correlation coefficient between flooding spatial distribution and hydrological stations on SPSS software. The results showed that the severe flood occurred from August to December in 2015. There were about 47.6% and 28.2% of the total area were inundated in October and August, respectively. The correlation between inundated areas and water level at Ha Tien and Chau Doc hydrological stations was 0.73 and 0.65 (p &amp;lt;0.01), respectively. The derived information was very essential and valuable for local managers in making decision on responding and mitigating to the flood disaster.</p>

scholarly journals Going beyond the flood insurance rate map: insights from flood hazard map co-production

2018 ◽  
Vol 18 (4) ◽  
pp. 1097-1120 ◽  
Author(s):  
Adam Luke ◽  
Brett F. Sanders ◽  
Kristen A. Goodrich ◽  
David L. Feldman ◽  
Danielle Boudreau ◽  
...  
Keyword(s):  
Flood Hazard ◽  
End Users ◽  
User Preferences ◽  
Flood Insurance ◽  
Hazard Map ◽  
Flood Hazards ◽  
Hazard Maps ◽  
Baseline Hazard ◽  
Flood Maps ◽  
Flood Hazard Maps

Abstract. Flood hazard mapping in the United States (US) is deeply tied to the National Flood Insurance Program (NFIP). Consequently, publicly available flood maps provide essential information for insurance purposes, but they do not necessarily provide relevant information for non-insurance aspects of flood risk management (FRM) such as public education and emergency planning. Recent calls for flood hazard maps that support a wider variety of FRM tasks highlight the need to deepen our understanding about the factors that make flood maps useful and understandable for local end users. In this study, social scientists and engineers explore opportunities for improving the utility and relevance of flood hazard maps through the co-production of maps responsive to end users' FRM needs. Specifically, two-dimensional flood modeling produced a set of baseline hazard maps for stakeholders of the Tijuana River valley, US, and Los Laureles Canyon in Tijuana, Mexico. Focus groups with natural resource managers, city planners, emergency managers, academia, non-profit, and community leaders refined the baseline hazard maps by triggering additional modeling scenarios and map revisions. Several important end user preferences emerged, such as (1) legends that frame flood intensity both qualitatively and quantitatively, and (2) flood scenario descriptions that report flood magnitude in terms of rainfall, streamflow, and its relation to an historic event. Regarding desired hazard map content, end users' requests revealed general consistency with mapping needs reported in European studies and guidelines published in Australia. However, requested map content that is not commonly produced included (1) standing water depths following the flood, (2) the erosive potential of flowing water, and (3) pluvial flood hazards, or flooding caused directly by rainfall. We conclude that the relevance and utility of commonly produced flood hazard maps can be most improved by illustrating pluvial flood hazards and by using concrete reference points to describe flooding scenarios rather than exceedance probabilities or frequencies.

Flash flood hazard mapping based on AHP with GIS and satellite information in Kampong Speu Province, Cambodia

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chhuonvuoch Koem ◽  
Sarintip Tantanee
Keyword(s):  
Land Use ◽  
Flash Flood ◽  
Flood Hazard ◽  
Geographical Information ◽  
Hazard Mapping ◽  
Hazard Map ◽  
Content Type ◽  
Satellite Information ◽  
Flood Hazard Map ◽  
Hazard Levels

Purpose Cambodia is considered one of the countries that are most vulnerable to adverse effects of climate change, particularly floods and droughts. Kampong Speu Province is a frequent site of calamitous flash floods. Reliable sources of flash flood information and analysis are critical in efforts to minimize the impact of flooding. Unfortunately, Cambodia does not yet have a comprehensive program for flash flood hazard mapping, with many places such as Kampong Speu Province having no such information resources available. The purpose of this paper is, therefore, to determine flash flood hazard levels across all of Kampong Speu Province using analytical hierarchy process (AHP) and geographical information system (GIS) with satellite information. Design/methodology/approach The integrated AHP–GIS analysis in this study encompasses ten parameters in the assessment of flash flood hazard levels across the province: rainfall, geology, soil, elevation, slope, stream order, flow direction, distance from drainage, drainage density and land use. The study uses a 10 × 10 pairwise matrix in AHP to compare the relative importance of each parameter and find each parameter’s weight. Finally, a flash flood hazard map is developed displaying all areas of Kampong Speu Province classified into five levels, with Level 5 being the most hazardous. Findings This study reveals that high and very high flash flood hazard levels are identified in the northwest part of Kampong Speu Province, particularly in Aoral, Phnum Srouch and Thpong districts and along Prek Thnot River and streams. Originality/value The flash flood hazard map developed here provides a wealth of information that can be invaluable for implementing effective disaster mitigation, improving disaster preparedness and optimizing land use.

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