Multi-criteria approach to develop flood susceptibility maps in arid regions of Middle East

2018 ◽  
Vol 196 ◽  
pp. 216-229 ◽  
Author(s):  
Shereif H. Mahmoud ◽  
Thian Yew Gan
Keyword(s):  
Middle East ◽  
Arid Regions ◽  
Flood Susceptibility ◽  
Susceptibility Maps

A Comparative Assessment of Multi-criteria Decision Analysis for Flood Susceptibility Modeling 

2021 ◽  
Author(s):  
Ehsan Shahiri Tabarestani ◽  
Hossein Afzalimehr
Keyword(s):  
Characteristic Curve ◽  
Drainage Density ◽  
Weight Of Evidence ◽  
Mazandaran Province ◽  
Conditioning Factors ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Susceptibility Maps ◽  
Reliable Model ◽  
First Time

Abstract Floods are one of the most damaging natural disasters throughout the world. The purpose of this study is to develop a reliable model for identification of flood susceptible areas. Three Multi-criteria decision-making techniques, namely Analytical Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Attributive Border Approximation Area Comparison (MABAC) methods combined with weight of evidence (WOE) were used in Mazandaran Province, Iran. MABAC method is applied to determine the flood susceptibility in this study, for the first time. At first, 160 flood locations were identified in the study area, of which 112 (70%) locations were selected randomly for modeling, and the remaining 48 (30%) locations were used for validation. Using Geographic Information System (GIS) with eight conditioning factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, the flood susceptibility maps were prepared. The results showed that the area under receiver operating characteristic curve (AUROC) for the test data of AHP-WOE, TOPSIS-WOE-AHP, and MABAC-WOE-AHP methods were 75.3%, 91.6%, and 86.1%, respectively, which indicate the reasonable accuracy of models. High accuracy of the proposed new model (MABAC) clarifies its applicability for preventive measures.

Fractal analysis of rain gauges’ inhomogeneous distribution and the associated hydrological impacts: the case study of Muriaé River basin

2021 ◽  
Author(s):  
Priscila Celebrini de O. Campos ◽  
Igor Paz ◽  
Maria Esther Soares Marques ◽  
Ioulia Tchiguirinskaia ◽  
Daniel Schertzer
Keyword(s):  
Fractal Dimension ◽  
Spatial Analysis ◽  
Fractal Analysis ◽  
Probability Of Detection ◽  
Susceptibility Map ◽  
Rain Gauges ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
Cross Tabulation ◽  
The Cross

<p>The urban population growth requires an improvement in the resilient behavior of these areas to extreme weather events, especially heavy rainfall. In this context, well-developed urban planning should address the problems of infrastructure, sanitation, and installation of communities, primarily related to insufficiently gauged locations. The main objectives of this study were to analyze the impacts of in-situ rain gauges’ distribution associated with the elaboration of a spatial diagnosis of the occurrence of floods in the municipality of Itaperuna, Rio de Janeiro – Brazil. The methodology consisted of the spatial analysis of rain gauges’ distribution with the help of the fractal dimension concept and investigation of flood susceptibility maps prepared by the municipality based on transitory factors (which consider precipitation in the modeling) and on permanent factors (natural flood susceptibility). Both maps were validated by the cross-tabulation method, crossing each predictive map with the recorded data of flood spots measured during a major rainfall event. The results pointed that the fractal analysis of the rain gauges’ distribution presented a scaling break behavior with a low fractal dimension at the small-scale range, mostly concerned in (semi-)urban catchments, highlighting the incapacity of the local instrumentation to capture the spatial rainfall variability. Thereafter, the cross-tabulation validation method indicated that the flood susceptibility map based on transitory factors presented an unsatisfactory probability of detection of floods when compared to the map based on permanent factors. These results allowed us to take into account the hydrological uncertainties concerning the insufficient gauge network and the impacts of the sparse distribution on the choice and elaboration of flood susceptibility maps that use rainfall data as input. Finally, we performed a spatial analysis to estimate the population and habitations that can be affected by floods using the flood susceptibility map based on permanent factors.</p>

scholarly journals Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China

2020 ◽  
Vol 9 (12) ◽  
pp. 748
Author(s):  
Yifan Cao ◽  
Hongliang Jia ◽  
Junnan Xiong ◽  
Weiming Cheng ◽  
Kun Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Flash Flood ◽  
Pearson Correlation ◽  
Flash Floods ◽  
Ensemble Model ◽  
Susceptibility Assessment ◽  
Certainty Factor ◽  
Fujian Province ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.

scholarly journals Assessing the susceptibility of schools to flood events in Iran

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Saleh Yousefi ◽  
Hamid Reza Pourghasemi ◽  
Sayed Naeim Emami ◽  
Omid Rahmati ◽  
Shahla Tavangar ◽  
...  
Keyword(s):  
Flood Risk ◽  
Flood Hazard ◽  
Rural Students ◽  
Flood Hazards ◽  
Analytic Hierarchy ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
High Flood ◽  
Analytic Hierarchy Process Method ◽  
Very High

Abstract Catastrophic floods cause deaths, injuries, and property damages in communities around the world. The losses can be worse among those who are more vulnerable to exposure and this can be enhanced by communities’ vulnerabilities. People in undeveloped and developing countries, like Iran, are more vulnerable and may be more exposed to flood hazards. In this study we investigate the vulnerabilities of 1622 schools to flood hazard in Chaharmahal and Bakhtiari Province, Iran. We used four machine learning models to produce flood susceptibility maps. The analytic hierarchy process method was enhanced with distance from schools to create a school-focused flood-risk map. The results indicate that 492 rural schools and 147 urban schools are in very high-risk locations. Furthermore, 54% of rural students and 8% of urban students study schools in locations of very high flood risk. The situation should be examined very closely and mitigating actions are urgently needed.

scholarly journals 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 ◽  
Vol 11 (13) ◽  
pp. 1589 ◽  
Author(s):  
Duie Tien Bui ◽  
Khabat Khosravi ◽  
Himan Shahabi ◽  
Prasad Daggupati ◽  
Jan F. Adamowski ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Vegetation Index ◽  
Slope Angle ◽  
Training Data ◽  
Multivariate Logistic Regression Model ◽  
Success Rates ◽  
Topographic Wetness Index ◽  
Validation Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.

scholarly journals EVALUATING THE VARIATIONS IN THE FLOOD SUSCEPTIBILITY MAPS ACCURACIES DUE TO THE ALTERATIONS IN THE TYPE AND EXTENT OF THE FLOOD INVENTORY

2017 ◽  
Vol XLII-4/W5 ◽  
pp. 209-214 ◽  
Author(s):  
M. Sh. Tehrany ◽  
S. Jones
Keyword(s):  
Area Under Curve ◽  
Natural Hazard ◽  
Susceptibility Map ◽  
Inventory Data ◽  
Flood Susceptibility ◽  
Susceptibility Maps ◽  
Rapid Processing ◽  
The Impact ◽  
Measurement Approach

This paper explores the influence of the extent and density of the inventory data on the final outcomes. This study aimed to examine the impact of different formats and extents of the flood inventory data on the final susceptibility map. An extreme 2011 Brisbane flood event was used as the case study. LR model was applied using polygon and point formats of the inventory data. Random points of 1000, 700, 500, 300, 100 and 50 were selected and susceptibility mapping was undertaken using each group of random points. To perform the modelling Logistic Regression (LR) method was selected as it is a very well-known algorithm in natural hazard modelling due to its easily understandable, rapid processing time and accurate measurement approach. The resultant maps were assessed visually and statistically using Area under Curve (AUC) method. The prediction rates measured for susceptibility maps produced by polygon, 1000, 700, 500, 300, 100 and 50 random points were 63 %, 76 %, 88 %, 80 %, 74 %, 71 % and 65 % respectively. Evidently, using the polygon format of the inventory data didn’t lead to the reasonable outcomes. In the case of random points, raising the number of points consequently increased the prediction rates, except for 1000 points. Hence, the minimum and maximum thresholds for the extent of the inventory must be set prior to the analysis. It is concluded that the extent and format of the inventory data are also two of the influential components in the precision of the modelling.

scholarly journals Dataset of 100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method

Data in Brief ◽  
2017 ◽  
Vol 12 ◽  
pp. 203-207 ◽  
Author(s):  
Caterina Samela ◽  
Salvatore Manfreda ◽  
Tara J. Troy
Keyword(s):  
Flood Susceptibility ◽  
Susceptibility Maps
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