Flood Susceptibility Analysis Research Articles

Flood Susceptibility Analysis Using Frequency Ratio Method in Walanae Watershed

Flood Susceptibility Analysis Using Frequency Ratio Method in Walanae Watershed

A GIS-based Binary Logistic Regression Model for the Inundation Analysis; A Case Study on Elapatha DS Division, Ratnapura District, Sri Lanka


 
 
 Flood susceptibility analysis (FSA) is a prerequisite for adopting flood mitigation and adaptation techniques. A number of technologies and models have emerged through time, and this study focuses on the Binary Logistic Regression Model (BLRM) to investigate flood vulnerability of Elapatha Divisional secretariat (DS), Ratnapura District, Sri Lanka where has high impact from inundation. Rainfall, land use and land cover (LULC), elevation, slope, slope aspect, distance to the river, topographic wetness index (TWI), and stream power index (SPI) were the factors used in the model construction. These components were investigated in terms of their contribution to flood susceptibility using all location data and field plotting of responsible parameters in the study area using Geographic Information System (GIS) software, and all extracted data points were 96489, of which 50% were used for BLRM development using SPSS statistical software and remaining 50% for model validation. The coefficient of rainfall parameters, log value of elevation in meters, the tan value of slope in degrees, radiant value of aspect in degrees, (distance from the river)0.1, the ratio between SPI and TWI (SPI/TWI) and LULC band values of built-up area, water bodies and vegetations were 0.023, -2.254, -1.018, -0.005, -0.164, -0.003, 2.707, -.067 and -0.004 respectively. The accuracy was validated using Mean standard error (MSE) and area under curve (AUC) analysis, with values of 0.031 and 0.724 respectively. The Built-up area, elevation, and slope had the most impact on the inundation Elapatha DS division, and model performance represents 72.4% accuracy. Therefore, mitigation of inundation problems can be achieved through proper landscaping in the area.
 Keywords: Binary logistic Regression, Flood susceptibility Analysis, GIS software, Spatial analysis
 
 

An evaluation on the parameters used in flood susceptibility analysis

Taşkınlar her geçen gün artan büyüklük ve sıklıklarına bağlı olarak dünyada ve ülkemizde önemi giderek artan afetlerden birisidir. Bu çalışmadaki temel amaç, taşkın duyarlılık ile ilgili uluslararası ve ulusal literatürün değerlendirilmesi ve duyarlılık çalışmalarına yeni bir yaklaşım olarak sel ve taşkınların meydana geldiği yerleşmelerin su toplama havzaları temelli taşkın duyarlılık parametrelerinin belirlenmesini gerçekleştirmektir. Bu kapsamda çalışmada tarihsel taşkın envanterine bağlı olarak Bursa ili sınırları içerisinde vadi tabanı ve akarsu kenarında sel ve taşkınların yaşandığı yerleşmelerin havzalarına bağlı olarak taşkın duyarlılık analizi parametreleri belirlenmiştir. Çalışmada kullanılan temel altlık veriler, Bursa iline ait 5m çözünürlüklü Sayısal Yükseklik Modeli (SYM), 1956-2022 yılları arasını kapsayan envanter verileri, litoloji, hidrolojik toprak grupları (HTG) ve yağış (WorldClim) verileridir. Bursa il sınırları içerisinde meydana gelen tarihsel sel ve taşkın envanterine bağlı olarak 28 yerleşme ve bu yerleşmelerin su toplama havzaları belirlenmiş ve bu havzalara sel ve taşkının oluşmasında hazırlayıcı 12 parametre uygulanmıştır. Taşkın hazırlayıcı parametreler sınıflandırma aşamasında 0-1 arasında normalize edilerek ortaya çıkan sonuca göre taşkın duyarlılık için parametre katsayıları oluşturulmuştur. Sonuç olarak envantere göre maksimum etkiye sahip parametreler; çatallanma oranı (R_b), drenaj yoğunluğu (D_d), akım toplanma zamanı (T_c), eğim, topografik nemlilik indeksi, akarsu güç indeksi, hidrolojik toprak grupları, olarak belirlenmiştir. Bu çalışma ile taşkın duyarlılık analizinde önceki çalışmalardan farklı olarak envantere bağlı ve yerleşim temelli havzalardan taşkın duyarlılık parametreleri belirlenmiştir.

Flood hazards and susceptibility detection for Ganga river, Bihar state, India: Employment of remote sensing and statistical approaches

Climate change and flooding are related issues on the Earth's surface, while numerous lowland areas, especially delta regions, are mostly affected by flood hazards. Hence, flood susceptibility mapping and the simulation of future effect areas are essential for hazard management and awareness. The river floodplain areas of the Ganga River in the Bihar state are the most affected due to high annual floods. Floods cause huge economic losses and environmental degradation, such as deforestation, riverbank erosion, and water quality loss. Thus, flood vulnerability measurement is a serious concern in this area, which involves building proper awareness and mitigation strategies to achieve sustainable development goals. Remote Sensing (RS) is widely applied to such hydrological issues. Hence, statistical approaches, such as the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and Fuzzy-AHP (FAHP) algorithms, were applied for flood susceptibility analysis in the selected flood plain of the Ganga River in the Bihar state. The most suitable flood susceptibility areas of three different approaches were 9604.21 km2 (AHP), 9712.48 km2 (FAHP) and 9598.28 km2 (FR), while river and channel areas were selected as not suitable area. The flooded maps indicated the flooded lands using Google Earth Engine (GEE) of different years are 2977.69 km2 (2020), 10481.63 km2 (2021), and 1103.89 km2 (2022), respectively. The results of the current study indicate that this area is essentially in need of serious attention for future hazard management and adaptation strategies for the reduction of flooded areas, in addition to the reduction of socio-economic variability in monsoon regions. Otherwise, floods destroyed cropland, increased food scarcity, and caused huge socio-economic losses.

GIS-based flood susceptibility analysis using multi-parametric approach of analytical hierarchy process in Majuli Island, Assam, India

GIS-based flood susceptibility analysis using multi-parametric approach of analytical hierarchy process in Majuli Island, Assam, India

GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India

The unique characteristics of drainage conditions in the Pagla river basin cause flooding and harm the socioeconomic environment. The main purpose of this study is to investigate the comparative utility of six machine learning algorithms to improve flood susceptibility and ensemble techniques' capability to elucidate the underlying patterns of floods and make a more accurate prediction of flood susceptibilities in the Pagla river basin. In the present scenario, the frequency of flood conditions in this study area becomes high with heavy and sudden rainfall, so it is essential to study flood mitigation and measure. At first, a spatial flood database was built with 200 flood locations and sixteen flood influencing factors, and its process with the help of the Geographic Information System (GIS) environment and build up different models applying the machine learning techniques. It has found different flood susceptibility zone using machine learning-based Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Reduced Error Pruning Tree (REPTree), Logistic Regression (LR), and Bagging helping GIS environment and the model validation using the Receiver Operating Characteristic (ROC) Curve. Afterward, ensemble all the models to gate comparative accuracy of the flood zone. The calculated areas are under the very high flood susceptibility zone 8.69%, 14.92%, 14.17%, 12.98%, 14.65%, 13.24% and 13.41% for ANN, SVM, RF, REPTree, LR and Bagging, respectively. Finally, ROC curve, the Standard Error (SE), and the Confidence Interval (CI) at 95 per cent were used to assess and compare the performance of the models. The obtained results indicate that all models are highly accepted Area Under Curve (AUC) of ROC between 0.889 (LR) to 0.926 (Ensemble). From the estimation of the accuracy of the applied methods using ROC, it is found that the Ensemble model has the higher capability compared to the other applied models in projecting flood susceptibility in ​the ​study ​area. It has the highest area under the ROC curve the AUC values are 0.918 and 0.926, the SE (0.023, 034), and the narrowest CI (95 per cent) (0.873–0.962, 0.859–0.993) whereas highest area under Bagging (the ROC) curve (AUC) value (0.914, 0.919), for both the training and validation datasets. After ensembling, the result shows that the result is a highly flood susceptible area located at the lower part of the study area. In this area, the very high flood susceptibility zone values lie between 4.46 and 6.00 in the ensemble result. The areas comprise the low height and belong to Murarai I, Murarai II, Suti I and Suti II C.D. block of West Bengal. The current study will help the policymakers and the researcher determine the flood conditioning problems for prospects.

Parameters and methods used in flood susceptibility mapping: a review

Abstract A correct understanding of the parameters and methods used in flood susceptibility mapping (FSM) is critical for identifying the strengths and limitations of different mapping approaches, as well as for developing methodologies. In this study, we examined scientific publications in the literature using WoS. Although the number of methods used is quite high, the number of parameters used in these methods varies, with a maximum of 21 and a minimum of 5 parameters preferred. It was found that the most commonly used parameter has a preference rate of 97%, but there is no common parameter in 100% of the studies. The methods used for determining flood susceptibility include multi-criteria decision-making (MCDM) methods, physically based hydrological models, statistical methods, and various soft computing methods. Although the use of traditional statistical methods and MCDM methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning. In the reviewed studies, it was observed that machine learning, fuzzy logic, metaheuristic optimization algorithms, and heuristic search algorithms, which are soft computing methods, have been widely used in FSM in recent years.

Flood Susceptibility Analysis in West Rapti River Basin Using Frequency Ratio Model

Floods are recognized as lethal natural events, which result in devastating natural and human losses. So, identifying flood susceptible zones is crucial to adopt necessary mitigation strategies beforehand. With the advent of GIS tools and modelling techniques, mapping of such zones has become easier and more precise; yet, flood prone countries like Nepal have not been able to embrace such tools for flood risk management. With a compelling need to address this situation, this paper employs Frequency Ratio model to analyze flood susceptibility in the West Rapti River Basin. The model, created with the help of 77 flood points and tested with 30 points to obtain 80.7% accuracy, maps the flood hazard zones in the area and identifies the lower Terai and settlement regions as high-risk areas. With the increasing threat of changing climate in the future, this study also propounds better preparation of flood inventory maps in the future for more precise susceptibility analysis models and better flood risk management.

A novel hybrid GIS‐based multi‐criteria decision‐making approach for flood susceptibility analysis in large ungauged watersheds

AbstractCharacterizing and identification of flood‐susceptible areas can be a solution to mitigate the damages and fatality rate. This study proposes a novel hybrid MCDM framework to assess flood susceptibility in large ungauged watersheds dealing with data scarcity issues. The proposed method examines the interdependencies and causal relationships between various criteria affecting the flooding procedure using the DEcision‐MAking Trial and Evaluation Laboratory (DEMATEL). Moreover, since experts' opinions contain uncertainty, the fuzzy logic is integrated with DEMATEL to overcome this shortcoming. Then, the local weights of criteria were estimated using the Best–Worst Method (BWM) to enhance the pairwise comparisons process. Final criteria weights were obtained using Fuzzy DEMATEL and BWM results in Analytical Network Process (ANP) super‐matrix. Finally, the criteria were distributed spatially using the Complex Proportional Assessment of Alternatives (COPRAS) method based on obtained weights. The proposed method was compared with different approaches such as Fuzzy‐DEMATEL ANP, BWM, and AHP using several statistical measures. We concluded that the novel hybrid proposed method outperformed other approaches based on our results. Moreover, by overlaying classified maps with the historical flood events locations, it was concluded that 85.96% of flooded areas were classified as “high” and “very high.”

Urban flood susceptibility analysis of Saroor Nagar Watershed of India using Geomatics-based multi-criteria analysis framework.

During the monsoon season, flooding is common in several parts of India, and urban areas are becoming more prone to flooding even during less intense rainfall events as a result of the encroachment of waterbodies and natural drainage channels, increased impervious areas, and subsequent decrease in infiltration capabilities. In the years 2000, 2008, 2016, 2019, 2020, and 2021, severe flood events in our study area, the Saroor Nagar urban watershed in Telangana state, caused human loss, economic devastation, and environmental devastation. Although flood risk cannot be completely eliminated, it can be significantly reduced by developing a flood hazard model to identify flood-prone areas in a watershed, which can assist decision makers seeking comprehensive flood risk management. The flood hazard map is created by integrating Geomatics with multi-criteria decision analysis and the analytical hierarchy process (AHP). Topographic wetness index (TWI), digital elevation model (DEM), slope, precipitation, drainage density, distance to waterbody, soil, and land use land cover (LULC) were the flooding causative elements considered in this study. The resulting flood risk map is divided into four distinct categories that reflect flood danger levels of low, moderate, high, and extremely high. The flood risk map revealed that the moderate risk zone decreased from 50.2 to 45.7% of the study area between 2008 and 2020, while the high-risk zone increased from 45.2 to 52.8%. The flood susceptibility map is validated using crowdsourcing techniques. The findings demonstrated that a Geographic Information System (GIS)-based multi-criteria analysis framework for flood hazard analysis could be effectively used to aid disaster management decision-making.

A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India

The Upper Krishna Basin in Maharashtra (India) is highly vulnerable to floods. This study aimed to generate a flood susceptibility map for the basin using Frequency Ratio and Statistical Index models of flood analysis. The flood hazard inventory map was created by 370 flood locations in the Upper Krishna Basin and plotted using ArcGIS 10.1 software. The 259 flood locations (70%) were selected randomly as training samples for analysis of the flood models, and for validation purposes, the remaining 111 flood locations (30%) were used. Flood susceptibility analyses were performed based on 12 flood conditioning factors. These were elevation, slope, aspect, curvature, Topographic Wetness Index, Stream Power Index, rainfall, distance from the river, stream density, soil types, land use, and distance from the road. The Statistical Index model revealed that 38% of the area of the Upper Krishna Basin is in the high- to very-high-flood-susceptibility class. The precision of the flood susceptibility map was confirmed using the receiver operating characteristic and the area under the curve value method. The area under the curve showed a 66.89% success rate and a 68% prediction rate for the Frequency Ratio model. However, the Statistical Index model provided an 82.85% success rate and 83.23% prediction rate. The comparative analysis of the Frequency Ratio and Statistical Index models revealed that the Statistical Index model was the most suitable for flood susceptibility analysis and mapping flood-prone areas in the Upper Krishna Basin. The results obtained from this research can be helpful in flood disaster mitigation and hazard preparedness in the Upper Krishna Basin.

Assessment of Flood Susceptibility Analysis Using Analytical Hierarchy Process (AHP) in Kota Belud Area, Sabah, Malaysia

This study aims to assess the flood susceptibility analysis using a Geographical Information System (GIS) based-heuristic analysis, namely the Analytical Hierarchy Process (AHP) model. Eight relevant physical parameters have been selected, namely, drainage density, drainage proximity, elevation, slope angle, slope curvature, land use, soil type, and topography wetness index. The relative importance of these factors has been compared in the pairwise matrix to gain weight values during the process of the Analytical Hierarchy Process (AHP). The flood susceptibility zones have been mapped according to their weightage value. Finally, the flood susceptibility map was prepared and classified into six classes as very low, low, moderate, high, and very high susceptibility using the natural break classification method. The accuracy of the flood susceptibility model was validated using the Area Under the Curve (AUC) analysis. The AUC for success rate was estimated at 82.13%.7.

A spatial model for coastal flood susceptibility assessment using the 2D-SPR method with complex network theory: A case study of a reclamation island in Zhoushan, China

Coastal floods are the type of marine disaster that causes the greatest economic losses in coastal areas of China, especially in the current context of global climate change. Mapping the spatial distribution of coastal flood susceptibility is a prerequisite for coastal flood risk analysis, and an important step in risk prevention and management. Based on the actual coastal flood propagation process, this study uses a 2D-source–pathway–receptor (2D-SPR) model to construct a complex network for coastal flood susceptibility analysis. In addition, it uses the decision-making trial and evaluation laboratory (DEMATEL) method and the technique for order preference by similarity to an ideal solution (TOPSIS) multi-attribute decision-making method to calculate the importance of each unit in the system, namely centrality Mi which ranges from 0.0716 to 4.3524, with a higher value indicating a higher importance. Finally, the spatial distribution of coastal flood susceptibility is drawn with ArcGIS software. For a clearer display, we divide the data into 12 levels according to the natural breakpoint method, and it can be seen that coastal flood susceptibility of different regions varies greatly. The classification results have obvious regularity, overall, open-land areas or parks, waters and southeast side have relatively higher values, which belong to the higher susceptibility area among the 12 levels. From the perspective of the integrity of the geographical unit, 15 locations including unbuilt areas and water areas are identified to have high flood susceptibilities relative to the mean values of all the areas. This study fully considers the hazard formation process of coastal floods, especially the complex interaction between hazard sources and receptors. These findings provide a new perspective for the rapid, objective assessment of coastal flood susceptibility.

Taşkın Duyarlılık Haritalarının Oluşturulmasında Kullanılan Yöntemler

In recent years, flood susceptibility mapping has an important place among the studies carried out to take precautions against floods and mitigate the damages and possible negative effects caused by floods. In this context, flood susceptibility analysis, especially on a regional scale, has been the subject of research by many researchers. In this study, the methods used in flood susceptibility mapping were investigated. 155 studies on flood susceptibility published between 2014 and 2022 were evaluated. In general, the methods used in the determination and evaluation of flood susceptibility are multi-criteria decision making (MCDM) methods, physically based hydrological models, statistical methods and various soft computing methods. Although the use rate of traditional statistical methods and multi-criteria decision making methods is already high among researchers, the methods used in flood susceptibility analysis have evolved over the years from traditional human judgments to statistical methods based on big data and machine learning methods. In the reviewed studies, it has been observed that machine learning, fuzzy logic, metaheuristic optimization algorithms and heuristic search algorithms, which are soft computing methods, have been widely used in the flood susceptibility mapping in recent years.

Flood Hazard Index Application in Arid Catchments: Case of the Taguenit Wadi Watershed, Lakhssas, Morocco

During the last decade, climate change has generated extreme rainfall events triggering flash floods in short periods worldwide. The delimitation of flood zones by detailed mapping generally makes it possible to avoid human and economic losses, especially in regions at high risk of flooding. The Taguenit basin, located in southern Morocco, is a particular case. The mapping of the flood zones of this basin by the method of the Flood Hazard Index (FHI) in a GIS geographic information systems environment was based on the multi-criteria analysis, taking into consideration the seven parameters influencing these extreme phenomena, namely rainfall, slope, flow accumulation, drainage network density, distance from rivers, permeability, and land use. Average annual rainfall data for 37 years (1980 to 2016) was used in this study for floodplain mapping. A weight was calculated for each parameter using the Analytical Hierarchy Process (AHP) method. The combination of the maps of the different parameters made it possible to draw up a final map classified into five risk intervals: very high, high, moderate, lower and very lower presenting, respectively, 8.04%, 20.63%, 31.47%, 15.36%, and 24.50% of the area of the basin. The reliability of this method was tested by a Flood susceptibility analysis. The results generated by the Flood Hazard Index (FHI) model are similar to those of previous historical events. Realistic and applicable solutions have been proposed to minimize the impact of these floods as much as possible.

Flood susceptibility zonation using advanced ensemble machine learning models within Himalayan foreland basin

Floods are considered as one of nature's most destructive fluvio-hydrological extremes because of the massive damage to agricultural land, roads and buildings and human fatalities. Rapid development of unplanned infrastructural conveniences and unplanned anthropogenic activities, the frequency and intensity of floods have been accelerated in recent years. Therefore, flood susceptibility analysis is considered as an important flood management approach. Identification of flood susceptibility areas has been performed by applying advanced machine learning (ML) algorithms (random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost)) at the lower part of Raidak river basin. The flood susceptibility maps have been generated based on 14 different flood conditioning factors. Models are evaluated in a conventional way using ROC (receiver operating characteristics) curve. The AUC value of ROC is above 0.80 for all models and XGBoost depicts the highest efficacy (AUC ​= ​0.92). Friedman test and Wilcoxon Signed rank test have been used to measure the statistical variances among the applied models. Models proficiently show that the upper part of Raidak river basin is a less flood probable region whereas the eastern and some middle parts have high flood probability. Around 27% area (285.39 sq.km) within the river basin is highly flood prone (based on XGBoost model) due to the fast changing dynamic landscape and large scale human intervention. The important outcomes of this research will definitely assist the local administrators to take proper sustainable management plans for the reduction of future damages.

Flood Susceptibility Assessment (FSA) Using GIS-Based Frequency Ratio (FR) Model in Kota Belud, Sabah, Malaysia

The flood is one of the most devastating natural disasters to strike Sabah, Malaysia, especially in the Kota Belud region. The Flood Susceptibility Analysis (FSA) was described using bivariate statistical analysis (the Frequency Ratio model) based on a Geographical Information System (GIS). Field surveys and formal reports from local authorities in the study area created the flood inventory map. The training dataset for statistical analysis consisted of 100 flood locations inundated in 2017, while the validation dataset consisted of 54 flood locations from the 2016 flood report. Eight (8) parameters (elevation, slope curvature, slope angle, topography wetness index, drainage density, drainage proximity, land use, and soil type) were extracted from the database and then converted into a raster format with a cell size of 5m x 5m. Finally, using the natural break classification method, the FSA was generated and classified into five classes: very low, low, moderate, high, and very high susceptibility. The area under the curve (AUC) analysis validated the flood susceptibility model's accuracy. The success rate AUC was calculated to be 0.89, while the prediction rate AUC was 0.82. The flood susceptibility analysis could be used to develop flood mitigation strategies in land use planning.

Flood Susceptibility Analysis using Geographic Information System, Frequency Ratio and Relative Frequency Ratio Methods in San Pa Tong District, Chiang Mai, Thailand

Flood Susceptibility Analysis using Geographic Information System, Frequency Ratio and Relative Frequency Ratio Methods in San Pa Tong District, Chiang Mai, Thailand

Flood susceptibility analysis of the Panjkora Valley Northern Pakistan, using frequencyratio approach

PurposeFloods are considered as one of the most lethal natural disasters having the potential to cause havoc to entire communities. Pakistan is the land of wide topographic and climatic variations which make it vulnerable to floods. The purpose of this paper is to identify flood susceptible zones in the Panjkora Basin using frequency ratio model.Design/methodology/approachA total of seven parameters or flood conditioning factors were considered, and weights were assigned according to the frequency ratio technique. For the preparation of layers, satellite imageries and digital elevation model data were used. Frequency ratio was calculated using correlation between these parameters and flood. Flood susceptibility index map was divided into five zones through quantile method in ArcMap.FindingsFindings of the study reveal that near half of the area (43%) is located in the very high susceptible zone, while only 20% area is classified as low to very low susceptible.Originality/valueThis paper is entirely based on original research. The approach used in this study has not been applied to the study area before.

A multi-source 120-year US flood database with a unified common format and public access

Abstract. Despite several flood databases available in the United States, there is a benefit to combine and reconcile these diverse data sources into a comprehensive flood database with a unified common format and easy public access in order to facilitate flood-related research and applications. Typically, floods are reported by specialists or media according to their socioeconomic impacts. Recently, data-driven analysis can reconstruct flood events based on in situ and/or remote-sensing data. Lately, with the increasing engagement of citizen scientists, there is the potential to enhance flood reporting in near-real time. The central objective of this study is to integrate information from seven popular multi-sourced flood databases into a comprehensive flood database in the United States, made readily available to the public in a common data format. Natural language processing, geocoding, and harmonizing processing steps are undertaken to facilitate such development. In total, there are 698 507 flood records in the United States from 1900 to the present, which highlights the longest and most comprehensive recording of flooding across the country. The database features event locations, durations, date/times, socioeconomic impacts (e.g., fatalities and economic damages), and geographic information (e.g., elevation, slope, contributing area, and land cover types retrieved from ancillary data for given flood locations). Finally, this study utilizes the flood database to analyze flood seasonality within major basins and socioeconomic impacts over time. It is anticipated that thus far the most comprehensive yet unified database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, hydroclimatic studies concerning spatiotemporal patterns of floods, and flood susceptibility analysis for vulnerable geophysical locations. The dataset is publicly available with the following DOI: https://doi.org/10.5281/zenodo.4547036 (Li, 2020).