Flash flood susceptibility modeling for drainage basins of Dir Lower Khyber-Pakhtunkhwa: a comparative analysis of morphometric ranking and El-Shamy’s approach

2020 ◽  
Author(s):  
Waqas Ahmad ◽  
Muhammad Jamal Nasir ◽  
Javed Iqbal
Keyword(s):  
Comparative Analysis ◽  
Flash Flood ◽  
Drainage Basins ◽  
Khyber Pakhtunkhwa ◽  
Flood Susceptibility ◽  
Susceptibility Modeling

scholarly journals Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms

2020 ◽  
Vol 12 (21) ◽  
pp. 3568
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Flash Flood ◽  
Flood Hazard ◽  
Machine Learning Algorithms ◽  
Mitigation Strategies ◽  
Boosted Regression Tree ◽  
Flash Flooding ◽  
Flood Susceptibility ◽  
Susceptibility Modeling

Flash flooding is considered one of the most dynamic natural disasters for which measures need to be taken to minimize economic damages, adverse effects, and consequences by mapping flood susceptibility. Identifying areas prone to flash flooding is a crucial step in flash flood hazard management. In the present study, the Kalvan watershed in Markazi Province, Iran, was chosen to evaluate the flash flood susceptibility modeling. Thus, to detect flash flood-prone zones in this study area, five machine learning (ML) algorithms were tested. These included boosted regression tree (BRT), random forest (RF), parallel random forest (PRF), regularized random forest (RRF), and extremely randomized trees (ERT). Fifteen climatic and geo-environmental variables were used as inputs of the flash flood susceptibility models. The results showed that ERT was the most optimal model with an area under curve (AUC) value of 0.82. The rest of the models’ AUC values, i.e., RRF, PRF, RF, and BRT, were 0.80, 0.79, 0.78, and 0.75, respectively. In the ERT model, the areal coverage for very high to moderate flash flood susceptible area was 582.56 km2 (28.33%), and the rest of the portion was associated with very low to low susceptibility zones. It is concluded that topographical and hydrological parameters, e.g., altitude, slope, rainfall, and the river’s distance, were the most effective parameters. The results of this study will play a vital role in the planning and implementation of flood mitigation strategies in the region.

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran

2018 ◽  
Vol 627 ◽  
pp. 744-755 ◽  
Author(s):  
Khabat Khosravi ◽  
Binh Thai Pham ◽  
Kamran Chapi ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
...  
Keyword(s):  
Decision Trees ◽  
Flash Flood ◽  
Comparative Assessment ◽  
Northern Iran ◽  
Flood Susceptibility ◽  
Susceptibility Modeling

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.

Comparison of statistical and analytical hierarchy process methods on flood susceptibility mapping: In a case study of the Lake Tana sub-basin in northwestern Ethiopia

2021 ◽  
Vol 13 (1) ◽  
pp. 1668-1688
Author(s):  
Azemeraw Wubalem ◽  
Gashaw Tesfaw ◽  
Zerihun Dawit ◽  
Belete Getahun ◽  
Tamrat Mekuria ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Analytical Hierarchy Process ◽  
Flood Hazard ◽  
Google Earth ◽  
Information Value ◽  
Lake Tana ◽  
Ahp Model ◽  
Flood Susceptibility ◽  
Susceptibility Modeling ◽  
Hierarchy Process

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.

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.

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