scholarly journals Comparison of Statistical and Analytical Hierarchy Process Methods on Flood Susceptibility Mapping: In a Case Study of the Tana Sub-basin in Northwestern Ethiopia

Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamirat Mekuria ◽  
...  

Abstract The sub-basin of Lake Tana is one of the most flood-prone areas in northwestern Ethiopia, which is affected by flood hazards. Flood susceptibility modeling in this area is essential for hazard reduction purposes. For this, the analytical hierarchy process (AHP), bivariate, and multivariate statistical methods were used. Using an intensive field survey, historical record, and Google Earth Imagery, 1404 flood locations were determined which are classified into 70% training datasets and 30% testing flood datasets using subset in the GIS tool. The statistical relationship between the probability of flood occurrence and eleven flood-driving factors is performed using the GIS tool. Then, the flood susceptibility map of the area is developed by summing all weighted factors using a raster calculator and classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The results for the area under the curve (AUC) are 99.1% for the frequency ratio model is better than 86.9% using AHP, 81.4% using the logistic regression model, and 78.2% using the information value model. Based on the AUC values, the frequency ratio (FR) model is relatively better followed by the AHP model for regional flood use planning, flood hazard mitigation, and prevention purposes.

2020 ◽  
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamirat Mekuria ◽  
...  

Abstract. The sub-basin of Lake Tana is one of the most flood-prone areas in northwestern Ethiopia, which is affected by flood hazards. Flood susceptibility modeling in this area is essential for hazard reduction purposes. For this, the analytical hierarchy process (AHP), bivariate, and multivariate statistical methods were used. Using an intensive field survey, historical record, and Google Earth Imagery, 1404 flood locations were determined which are classified into 70 % training datasets and 30 % testing flood datasets using subset in the GIS tool. The statistical relationship between the probability of flood occurrence and eleven flood-driving factors is performed using the GIS tool. Then, the flood susceptibility map of the area is developed by summing all weighted factors using a raster calculator and classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The results for the area under the curve (AUC) are 99.1 % for the frequency ratio model is better than 86.9 % using AHP, 81.4 % using the logistic regression model, and 78.2 % using the information value model. Based on the AUC values, the frequency ratio (FR) model is relatively better followed by the AHP model for regional flood use planning, flood hazard mitigation, and prevention purposes.


2021 ◽  
Vol 13 (1) ◽  
pp. 1668-1688
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamrat Mekuria ◽  
...  

Abstract The flood is one of the frequently occurring natural hazards within the sub-basin of Lake Tana. The flood hazard within the sub-basin of Lake Tana causes damage to cropland, properties, and a fatality every season. Therefore, flood susceptibility modeling in this area is significant for hazard reduction and management purposes. Thus, the analytical hierarchy process (AHP), bivariate (information value [IV] and frequency ratio [FR]), and multivariate (logistic regression [LR]) statistical methods were applied. Using an intensive field survey, historical document, and Google Earth Imagery, 1,404-flood locations were determined, classified into 70% training datasets and 30% testing flood datasets using a subset within the geographic information system (GIS) environment. The statistical relationship between the probability of flood occurrence and 11 flood-driving factors was performed using the GIS tool. The flood susceptibility maps of the study area were developed by summing all weighted aspects using a raster calculator. It is classified into very low, low, moderate, high, and very high susceptibility classes using the natural breaks method. The accuracy and performance of the models were evaluated using the area under the curve (AUC). As the result indicated, the FR model has better performance (AUC = 99.1%) compared to the AHP model (AUC = 86.9%), LR model (AUC = 81.4%), and IV model (AUC = 78.2%). This research finds out that the applied methods are quite worthy for flood susceptibility modeling within the study area. In flood susceptibility modeling, method selection is not a serious challenge; the care should tend to the input parameter quality. Based on the AUC values, the FR model is comparatively better, followed by the AHP model for regional land use planning, flood hazard mitigation, and prevention purposes.


Author(s):  
E. Tazik ◽  
Z. Jahantab ◽  
M. Bakhtiari ◽  
A. Rezaei ◽  
S. Kazem Alavipanah

Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the <b>combining the three methods</b> Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.


Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 525
Author(s):  
Alaa Ahmed ◽  
Chathuri Ranasinghe-Arachchilage ◽  
Abdullah Alrajhi ◽  
Guna Hewa

In semi-arid regions, groundwater resources play a crucial role in all economic, environmental, and social processes. However, the occurrence, movement, and recharge of these hidden and valuable resources vary from place to place. Therefore, better management practices and mapping of groundwater recharge potential zones are needed for the sustainable groundwater resources. For an example, groundwater resources in Willochra Basin are vitally important for drinking, irrigation, and stock use. This study shows the significance of the application of three decision-making approaches, including multi-influencing factor, analytical hierarchy process, and frequency ratio techniques in the identification of groundwater potential zones. A total of seven criteria, including lithology, slope, soil texture, land-use, rainfall, drainage density, and lineament density, were extracted from conventional and remote sensing data sources. The parameters and their assigned weights were integrated using Geographic Information System (GIS) software to generate recharge potential maps. The resultant maps were evaluated using the area under the curve method. The results showed that the southern regions of the Willochra Basin are more promising for groundwater recharge potential. The map produced using the frequency ratio model was the most efficient (84%), followed by the multi-influencing factor model (70%) and then the analytical hierarchy process technique (62%). The area under the curve method agreed when evaluated using published weights and rating values.


2021 ◽  
Vol 930 (1) ◽  
pp. 012095
Author(s):  
R Aprilia ◽  
E Hidayah ◽  
D Junita K

Abstract Flood is one of the disaster threats downstream of Welang river, Pasuruan. A flood susceptibility map is needed to anticipate floods disasters. This research aimed to map flood Susceptibility in the Welang watershed using a Geographical Information System. In determining flood hazard, the Frequency Ratio (FR) approach was used. Flood locations were identified from the interpretation of field survey data as training data and model validation. The data were represented in a Digital Elevation Model (DEM) map, geological data, land use, river data, and Landsat Satellite Imagery and processed into a spatial database on the GIS platform. The factors that caused flooding consisted of Flood inventory, slope, Elevation, Topographic Wetness Index (TWI), Standardized Precipitation Index (SPI), Flow Accumulation, Distance to the river, River Density, Rainfall, Vegetation Index (NDVI), and Landuse. The map results with acceptable accuracy showed that the FR model gained an Area Under Curve (AUC) value of 90%, and the incidence for the Area Under Curve ( AUC ) was 93%. It is known that 1% of the flood-prone area is very high. The local Government can use the research to minimize the risk of flooding in the Welang watershed.


Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 758 ◽  
Author(s):  
Romulus Costache ◽  
Alina Barbulescu ◽  
Quoc Bao Pham

In the present study, the susceptibility to flash-floods and flooding was studied across the Izvorul Dorului River basin in Romania. In the first phase, three ensemble models were used to determine the susceptibility to flash-floods. These models were generated by a combination of three statistical bivariate methods, namely frequency ratio (FR), weights of evidence (WOE), and statistical index (SI), with fuzzy analytical hierarchy process (FAHP). The result obtained from the application of the FAHP-WOE model had the best performance highlighted by an Area Under Curve—Receiver Operating Characteristics Curve (AUC-ROC) value of 0.837 for the training sample and another of 0.79 for the validation sample. Furthermore, the results offered by FAHP-WOE were weighted on the river network level using the flow accumulation method, through which the valleys with a medium, high, and very high torrential susceptibility were identified. Based on these valleys’ locations, the susceptibility to floods was estimated. Thus, in the first stage, a buffer zone of 200 m was delimited around the identified valleys along which the floods could occur. Once the buffer zone was established, ten flood conditioning factors were used to determine the flood susceptibility through the analytical hierarchy process model. Approximately 25% of the total delimited area had a high and very high flood susceptibility.


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