Flood Inventory Research Articles

Towards urban flood susceptibility mapping using data-driven models in Berlin, Germany

Identifying urban pluvial flood-prone areas is necessary but the application of two-dimensional hydrodynamic models is limited to small areas. Data-driven models have been showing their ability to map flood susceptibility but their application in urban pluvial flooding is still rare. A flood inventory (4333 flooded locations) and 11 factors which potentially indicate an increased hazard for pluvial flooding were used to implement convolutional neural network (CNN), artificial neural network (ANN), random forest (RF) and support vector machine (SVM) to: (1) Map flood susceptibility in Berlin at 30, 10, 5, and 2 m spatial resolutions. (2) Evaluate the trained models' transferability in space. (3) Estimate the most useful factors for flood susceptibility mapping. The models' performance was validated using the Kappa, and the area under the receiver operating characteristic curve (AUC). The results indicated that all models perform very well (minimum AUC = 0.87 for the testing dataset). The RF models outperformed all other models at all spatial resolutions and the RF model at 2 m spatial resolution was superior for the present flood inventory and predictor variables. The majority of the models had a moderate performance for predictions outside the training area based on Kappa evaluation (minimum AUC = 0.8). Aspect and altitude were the most influencing factors on the image-based and point-based models respectively. Data-driven models can be a reliable tool for urban pluvial flood susceptibility mapping wherever a reliable flood inventory is available.

Flood Risk and Exposure: The Case of the Tajuña Valley (Madrid, Spain)

This paper aims to update the exposure to flood risk in a catchment area of the Community of Madrid (Spain) linked to primary sector activities, albeit affected by the urban expansion of the capital. This research starts with the updating of the flood inventory, encompassing episodes documented between 1629 and 2020. The inadequate occupation of the territory means that floods continue to cause significant damage nowadays. It is worth highlighting the two recent floods (2019) that occurred just 15 days apart and caused serious damage to several towns in the basin. The areas at risk of flooding are obtained from the National Floodplain Mapping System, and the maximum and minimum floodable volume in the sector of the Tajuña River basin with the highest exposure to flooding has been calculated. The Sentinel 2 image in false colour (RGB bands 11-2-3, 11-8-3 and 12-11-8) and its transformation to colour properties (Intensity, Hue and Saturation) has made it possible to determine the extension of the riparian vegetation and the irrigated crops located in the alluvial plain. The SPOT 6 image with higher spatial resolution has allowed us to update the mapping of buildings located in areas at risk of flooding. Finally, based on cadastral data, a detailed cartography of built-up areas in areas at risk of flooding is provided. They affect buildings built mainly between the 1960s and 1990s, although the most recent buildings are built on agricultural land in the alluvial plain, even though current regulations prevent the occupation of these lands.

Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into 70%, used for training, and 30%, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Flash Flood Susceptibility Mapping of Sungai Pinang Catchment using Frequency Ratio

Flash flood are natural disasters that frequently occur in Malaysia especially in urban areas. Due to this, the development of flash flood susceptibility mapping one of the tools used to aid the local authority in reducing and managing the flash flood impact. Frequency Ratio (FR) is a popular method in predictive modeling because of its capabilities to determine the critical conditioning factor of flash flood. The aim of this research was to compare the standalone FR with Ensemble FR-AHP. This ensemble method uses pair-wise comparison method between Frequency Ratio and Analytical Hierarchy Process (AHP). For this research, ten conditioning factors were selected which were slope, aspect, curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), Normalized Difference Vegetation Index (NDVI), distance from river, rainfall, elevation, and land use/land cover (LULC). The flash flood inventory was obtained from local authorities where the flash flood occurred in Penang, Malaysia on November 2017. 70% of 110 flooded locations were used as training dataset to assess the spatial distribution of flooding whereas the remaining 30% flooded locations were used as validation dataset. Based on the results, the prediction rate of FR-AHP method is slightly better accuracy compared to FR method which 88.33% (FR-AHP) and 85.62% (FR). The output of this research is crucial to assist local authority in land use planning and drainage system of the study area.

Flood Risk Mapping using integrated fuzzy membership and Analytical Hierarchy Process at Sarpang district in Bhutan

Flood risk mapping is a preliminary step to evaluate the flood risk of an area. This study aimed at developing a flood risk map (FRM) at Sarpang district in Bhutan using integrated fuzzy membership and the Analytical Hierarchy Process (AHP). A total of 110 flood points were identified during flood inventory which is divided into a training dataset (70%) and validation dataset (30%). The fuzzy membership was used to reclassify factors which were calculated from the frequency ratio. The AHP was used to calculate the weight of the factors. The weighted reclassified factors were added to form the FRM. The FRM was validated using the accuracy and Kappa index. The accuracy shows the proportion of correctly classified flood and non-flood pixels while Kappa index shows the reliability of the model used. The accuracy is 89.39% indicating good result from the generated FRM. The Kappa index value 0.7879 falls under the substantial reliability category of the model. The result of FRM is expected to guide for the purpose of decision-making and planning in the future.

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

Frequency ratio application for mapping flood susceptibility in Welang Watershed, East Java

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.

Improving urban flood susceptibility mapping using transfer learning

The flood inventory in urban areas is often difficult to collect and therefore inadequate for training a machine learning (ML)-based assessment model. In this study, we investigated the utility of transfer learning (TL) to improve urban flood susceptibility assessment using knowledge outside the training domain. Specifically, we pre-trained a convolutional neural network-based assessment model in one catchment and then tried to improve the model performance of two other catchments by transferring knowledge from the pre-trained model. The results showed that (1) with the help of TL, the model performance of the two transferred catchments improved by 10%–25% in different data-rich and data-sparse scenarios; and (2) TL typically provided positive effects when we transferred knowledge from a well pretrained model in data-rich catchment. However, it may induce negative effects when the pre-trained model has poor accuracy. This study provides a promising approach to improve ML-based hazard assessment by sharing knowledge from different regions.

XGBoost-based method for flash flood risk assessment

Flash flood risk assessment, a widely applied technology in preventing catastrophic flash flood disasters, has become the current research hotspot. However, most existing machine learning methods for assessing flash flood risk rely on a single classifier, which is suitable for processing small sets of sample data, but the resulting prediction accuracy and generalization ability are insufficient. Meanwhile, machine learning methods that integrate multiple classifiers are thus far unknown. Extreme Gradient Boosting (XGBoost) is an excellent algorithm for ensemble learning methods which has achieved remarkable results in many fields. It not only optimizes the algorithm but also automatically applies the CPU's multi-threading to perform parallel calculations, thus greatly improving the model training speed and prediction accuracy. Therefore, this article introduces the XGBoost model for the assessment of flash flood risk, and then combines the two input strategies and the Least squares support vector machine (LSSVM) model to verify its optimal effect, thus proposing the XGBoost-based method for flash flood risk assessment. Subsequently, an attribution analysis was implemented to assess the possible errors of this approach; and finally, a county-level flash flood risk map for Yunnan Province, China, was generated based on the proposed method. The results demonstrate that: (1) XGBoost performs well, with an accuracy of 0.84 in the testing period, and its five indices (precision, recall, accuracy, kappa, and F-score) are all higher than those of LSSVM. (2) The XGBoost-based approach provided the reliable flash flood risk maps, which were validated by another flash flood inventory, although some errors may be attributed to critical environmental factors and statistical disaster location accuracy. (3) The high-risk counties (including high-risk and highest-risk) accounted for 40.3%, with the highest-risk counties mainly concentrated in southeastern Yunnan. This article further addresses the limitations of XGBoost (e.g., as a time-consuming greedy algorithm, the non-necessity of multi-threaded optimization). All of the above results indicate that the XGBoost-based method is an effective method for obtaining high-quality county-level flash flood risk maps, which contributes to the theoretical basis for ongoing county-level flash flood prevention in China.

India flood inventory: creation of a multi-source national geospatial database to facilitate comprehensive flood research

Floods are one of the most devastating natural hazards across the world, with India being one of the worst affected countries in terms of fatalities and economic damage. In-depth research is required in order to understand the complex hydrometeorological and geomorphic factors at play and design solutions to minimize the impact of floods. But the existence of a historical inventory of floods is imperative to promote such research endeavors. Though, a few global inventories exist, they lack the spatio-temporal fidelity necessary to make them useful for computational research due to reasons such as concentrating exclusively on large floods, limited temporal scope, non-standard data formats. Therefore, there is an urgent need for developing a new database that combines data from global and hitherto-underutilized local datasets using an extensible and common schema. This paper describes the ongoing effort of building the India Flood Inventory (IFI), which is the first freely available, analysis-ready geospatial dataset over the region with detailed qualitative and quantitative information regarding floods, including spatial extents. The paper outlines the methodology that has been adopted as well as some preliminary findings using the data contained in this inventory. This dataset is expected to advance the understanding of flood processes in the worst affected region of the world.

Evaluación de peligro de inundación utilizando AHP en Corum, Turquía

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.

Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia

Identification of areas susceptible to floods is an important issue which requires an increased attention due to the changing frequency and magnitude of floods, which is mainly a result of the ongoing climate change and increasing anthropic pressure on the landscape. In this study, the aim was to identify the areas susceptible to floods using and comparing two different approaches, namely the multi-criteria decision analysis-analytical hierarchy process (MCDA-AHP) and the machine learning-boosted classification (BCT) and boosted regression (BRT) tree. The study area was represented by the Topľa river basin, Slovakia. Altogether, seven relevant flood conditioning factors: elevation, slope, river network density, distance from river, flow accumulation, curve numbers and lithology as well as flood inventory database consisting of 107 flood locations were used. Based on the results, almost 40% of the study area is characterized by high to very high flood susceptibility using the MCDA-AHP. In case of the BCT and BRT models, the share of high and very high flood susceptibility class on the basin area is 45% and 38%, respectively. Validation of the performed flood susceptibility models confirmed generally higher accuracy of the machine learning models. The accuracy of the MCDA-AHP model was 81.33% while the accuracy of the boosted tree models was 87.70% and 91.42%, respectively, for classification and regression. The results of this study can enhance more effective preliminary flood risk assessment according to the EU Floods Directive.

Urban flood susceptibility assessment based on convolutional neural networks

In this study, a convolutional neural network (CNN)-based approach is proposed to assess flood susceptibility for urban catchment. Nine explanatory factors covering precipitation, topographical, and anthropogenic aspects were selected and two CNNs, SCNN and LeNet-5, were implemented to identify the relationship between the explanatory factors and flood inventory between 2004 and 2014 in the Dahongmen catchment in Beijing, China. The performance of the CNNs was compared with that of support vector machine (SVM) and random forest (RF) models for three model input strategies (point-based, array-based and imaged-based strategies). The results showed that: (1) The two CNNs performed better than the SVM and RF, with an accuracy of 0.90 for SCNN and 0.88 for Lenet-5 in the testing period. (2) The CNN-based approach provided more reliable flood susceptibility maps than the comparative models with the highest area under the curve (AUC) index of 0.9 validating by another flood inventory. (3) The upstream inundations induced by pluvial flood cannot be accurately identified by using point-based and array-based strategies. These errors were corrected by considering topographical information using the imaged-based CNN approach. (4) The fixed architecture LeNet-5 produced satisfactory results and avoided the time-consuming process of architecture selection of CNN. We conclude that the proposed CNN-based approach is a valid approach and provides a high-quality susceptibility map for urban mitigation and flood management.

A novel per pixel and object-based ensemble approach for flood susceptibility mapping

Conducting flood susceptibility assessments is critical for the identification of flood hazard zones and the mitigation of the detrimental impacts of floods in the future through improved flood management measures. The significance of this study is that we create ensemble methods using the per-pixel approaches of frequency ratio (FR), analytical hierarchical process (AHP), and evidence belief function (EBF) used for weightings with the object-based ‘geons’ approach used for aggregation to create a flood susceptibility map for the East Rapti Basin in Nepal. We selected eight flood conditioning factors considered to be relevant in the study area. The flood inventory data for the East Rapti basin was derived from past flood inventory datasets held in the regional database system by the International Centre for Integrated Mountain Development (ICIMOD). The flood inventory was classified into training and validation datasets based on the widely used split ratio of 70/30. The Receiver Operating Characteristic (ROC) was used to determine the accuracy of the flood susceptibility maps. The AUC results indicated that the combined per-pixel and object-based geon approaches yielded better results than the per-pixel approaches alone. Our results showed that the object-based geon approach creates meaningful regional units that are beneficial for future planning.

Spatio-Statistical Analysis of Flood Susceptibility Assessment Using Bivariate Model in the Floodplain of River Swat, District Charsadda, Pakistan

Flood is one of the most predominant disasters around the globe and frequently occurring phenomena in the northern part of Pakistan. In this study, the effects of various divisions of flood inventory and combinations of conditioning factors were assessed for the preparation of final susceptibility map. The flood inventory map was prepared for Charsadda by visual interpretation of Landsat-7 image alongside the field survey and a total of 161 flood locations were mapped. The flood inventory was subsequently divided into training and validation datasets, 129 (80%) and 112 (70%) locations for training the model and 32 (20%) and 49 (30%) for validation of the model. In this study, nine conditioning factors were used (Elevation, Slope, Aspect, Curvature, Plan curvature, Profile curvature, Proximity to river, roads, and Land use/land cover) for the development of flood susceptibility map. All the conditioning factors were correlated with flood inventory map using the information value method. The final susceptibility maps were validated using prediction rate and success rate curve. The results from validation showed that the areas under curve in the prediction rate curve for the models are: Model A (99.47%), Model B (95.04%), and Model C (94.06%), respectively. The Area under curve (AUC) in the success rate curve obtained for the three models are: Model A (95.03%), Model B (86.91%), and Model C (89.67%), respectively. Eventually, the susceptibility maps were classified into five susceptibility zones. The success rate and prediction rate curve indicated that model A has more accuracy in comparison to model B and model C; though, the results obtained from prediction and success rate curve indicated that all the models are reliable and has no significant difference between the susceptibility maps. Consequently, results obtained from this study are useful for researchers, disaster managers, and decision-makers to manage the flood-prone areas in the study area to mitigate the flood damages.

Multiple environmental factors analysis of flash flood risk in Upper Hanjiang River, southern China.

Identifying the environmental factors and analyzing the causal mechanism of flash floods help to manage the risk. Maximum 24-h precipitation (MP), digital elevation (DE), slope degree (SD), soil type (ST), drainage density (DD), and vegetation cover (VC) are selected as the risk factors of flash floods in this study. Precipitation is the important meteorological components in flash floods; thus spatial characteristics of precipitation trend have been analyzed by using Mann-Kendall tests, and a positive trend of precipitation in Upper Hanjiang River is detected. Then, association rule mining approach is proposed to investigate the multiple environmental factors of flash floods, in which both single and multiple dimension data mining have been conducted by Apriori algorithm. Considering the high rate of 5-year return period floods in the flash flood inventory, further association rule mining after sampling has been conducted in order to deeply mine the causal patterns of flash floods in different risk magnitudes. Results show that soil type, slope degree, and digital elevation are the dominant environmental factors of flash floods in the study area, and precipitation is one of the important causal factors in severe flash flood hazards. It is also highlighted that flash floods might easily occur even with a slight rainfall present due to the instability of sand clay and saturated soil moisture. The proposed novel use of field data and data mining has the potential for providing procedures and solutions for an effective interpretation of flash flood mechanism. The results are expected to be applicable for decision-making and sustainable management in flooding risk.

Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis

This work proposes a new approach by integrating statistical, machine learning, and multi-criteria decision analysis, including artificial neural network (ANN), logistic regression (LR), frequency ratio (FR), and analytical hierarchy process (AHP). Dependent (flood inventory) and independent variables (flood causative factors) were prepared using remote sensing data and the Mike-11 hydrological model and secondary data from different sources. The flood inventory map was randomly divided into training and testing datasets, where 334 flood locations (70%) were used for training and the remaining 141 locations (30%) were employed for testing. Using the area under the receiver operating curve (AUROC), predictive power of the model was tested. The results revealed that LR model had the highest success rate (81.60%) and prediction rate (86.80%), among others. Furthermore, different combinations of the models were evaluated for flood susceptibility mapping and the best combination (11C) was used for generating a new flood hazard map for Bangladesh. The performance of the 11C integrated models was also evaluated using the AUROC and found that integrated LR-FR model had the highest predictive power with an AUROC value of 88.10%. This study offers a new opportunity to the relevant authority for planning and designing flood control measures.

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

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.

Flood susceptibility modeling and hazard perception in Rwanda

Flooding is a deleterious phenomenon that induces detrimental impacts on humans, properties and environment. As a result, the knowledge of susceptible places and hazard perception is increasingly pertinent. This study mainly aims at identifying areas susceptible to flood through the application of logistic regression model using remote sensing data (RS) and Geographical Information System (GIS). A flood inventory was generated using 153 historical flood locations and a total of 10 predicting factors (elevation, slope, aspect, profile curvature, distance from rivers, distance from roads, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI), the Topographic Wetness Index (TWI) and rainfall) were utilized. Flood points were randomly subdivided into training (75%) for model building and testing (25%) points for validation through the Area Under Curve (AUC) approach. The results indicated that NDVI and rainfall are the most influencing variables for estimating flood risk as they showed a high positive relationship with flood occurrence in the study area. Testing datasets disclosed 79.8% of prediction rate using the AUC. Moreover, the results have been linked with community perception on flood and the outcome revealed that the government is perceived as responsible for all flood mitigation measures instead of being a shared responsibility. This perception may contribute to the increase in susceptibility. The results of this study will be essential for upcoming development projects from different organizations operating in many developing countries and would assist as a baseline for flood risk reduction and management especially for Rwanda.

Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods

Statistical methods are the most popular techniques to model and map flood-prone areas. Although a wide range of statistical methods have been used, application of the statistical index (Wi) method has not been examined in flood susceptibility mapping. The aim of this research was to assess the efficiency of the Wi method and compare its outcomes with the results of frequency ratio (FR) and logistic regression (LR) methods. Thirteen factors, namely, altitude, slope, aspect, curvature, geology, soil, landuse/cover (LULC), topographic wetness index (TWI), stream power index (SPI), terrain roughness index (TRI), sediment transport index (STI), and distance from rivers and roads, were utilized. A flood inventory was constructed from data captured from the destructive flood that occurred in Brisbane, Australia, in 2011. Model performances were compared using the area under the curve (AUC), Kappa index and five other statistical evaluation tools. The AUC prediction rates acquired for LR, Wi and FR were 79.45%, 78.18%, and 67.33%, respectively. A more realistic representation of the flood-prone area distribution was produced by the Wi method compared to those of the other two techniques. Our research shows that the Wi method can be used as an efficient approach to perform flood susceptibility analysis.