Flood Inventory Map Research Articles

Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping.

Artificial neural network and multi-criteria decision-making models for flood simulation in GIS: Mazandaran Province, Iran

Flood is one of the most destructive natural disasters globally and is a concern due to its high vulnerability. In this study for identification of flood susceptible areas, artificial neural network (ANN) and Multi-Attributive Border Approximation Area Comparison (MABAC) combined with Weights of Evidence (WoE) and Analytical Hierarchy Process (AHP) Models were used in Mazandaran province, Iran. MABAC method was used for the first time to evaluate the flood-prone areas in this study, and Attempts have been made for evaluate the performance of this new method by comparing with ANN model. The output of the neural network was discharge values in hydrometric stations. Using Geographic Information System (GIS) with eight effective factors including rainfall, distance from rivers, slope, soil, geology, elevation, drainage density, and land use, a flood model developed. Three precision parameters containing $${R}^{2}$$ , RMSE and MAE were applied to show the performance of the ANN model which yielded the values of 0.89, 0.0024 $${m}^{3}/s$$ , and 0.0018 $${m}^{3}/s$$ , respectively for testing data. The verification results indicated satisfactory agreement between the predicted and the real hydrological records. Also, based on flood inventory map and using the area under receiver operating curve, predictive power of the MABAC-WoE-AHP model was evaluated. The AUC value for prediction rate of this model was 86.1% which indicates the very good accuracy in predicting flood-prone areas. Comparison of flood susceptibility maps for ANN and MABAC-WoE-AHP models showed the good agreement between two models, that clarifies the efficiency of the new proposed method for future preventive measures.

Spatial modeling of flood probability using geo-environmental variables and machine learning models, case study: Tajan watershed, Iran

The main objective of this study was to produce flood susceptibility maps for Tajan watershed, Sari, Iran using three machine learning (ML) models including Self-Organization Map (SOM), Radial Basis Function Neural Network (RBFNN), and Multi-layers Perceptron (MLP). To reach such a goal, different physical-geographical factors (criteria) were integrated and mapped. 212 flood inventory map was randomly divided into training and testing datasets, where 148 flood locations (70%) were used for training and the remaining 64 locations (30%) were employed for testing. Model validation was performed using several statistical indices and the area under the curve (AUC). The results of the correlation matrix showed, three factors slope (0.277), distance from river (0.263), and altitude (0.223) were the most important factors affecting flood. The accuracy evaluation of the flood susceptibility maps through the AUC method and K-index shows that in the validation phase RBFNN (AUC = 0.90) outperform the MLP (AUC = 0.839) and SOM (AUC = 0.882) models. The highest percentage flood susceptibility of the area in MLP, SOM and RBFNN models is related to moderate (28.7%), very low (40%) and low (37%), respectively. Also, the validation results of the models using the Relative Flood Density (RFD) approach showed that very high class had the highest RFD value.

Flood Severity assessment of the coastal tract situated between Muriganga and Saptamukhi estuaries of Sundarban delta of India using Frequency Ratio (FR), Fuzzy Logic (FL), Logistic Regression (LR) and Random Forest (RF) models

Sundarban delta is one of the most flood sensitive areas of India. So, the study attempts to prepare a micro-level flood severity map (FSM) of low-lying active tidal plain situated between Muriganga and Saptamukhi estuaries of Sundarban delta using Frequency Ratio, Fuzzy Logic, Logistic Regression and Random Forest model. Flood inventory map consisting of 128 flood points were prepared and divided into training (70%) and testing (30%) classes. This study incorporates eight conditioning factors which include altitude, distance from bank line, distance from breaching points, geomorphology, Topographic Wetness Index, population density, distance from flood shelters and distance from roads. Further, reduction of model biasness due to collinearity among the causative factors was assessed. Subsequently, FSM maps were prepared using Geographical Information Systems (GIS). Lastly, model validation was done using Receiver Operating Characteristics (ROC) curve which shows that Fuzzy Logic and Random Forest are comparatively better in flood severity prediction in the study area than the other two models.

Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms

Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871).

Flood spatial prediction modeling using a hybrid of meta-optimization and support vector regression modeling

Flood spatial susceptibility prediction is the first essential step in developing flood mitigation strategies and reducing flood damage. Flood occurrence is a complex process that is not easily predicted through simple methods. This study describes optimization of support vector regression (SVR) using meta-optimization algorithms including the grasshopper optimization algorithm (GOA) and particle swarm optimization (PSO) for flood modeling at Qazvin Plain, Iran. Geospatial data including nine readily available geo-environmental flood conditioning factors (i.e., ground slope, aspect, elevation, planform curvature, profile curvature, proximity to a river, land use, lithology and rainfall) were derived. The information gain ratio (IGR) method was used to determine the relative importance of input variables. A historical flood inventory map for 43 locations was created from existing reports. The geospatial data and historical flood levels were used to construct the training and testing datasets. Then, the training dataset was used to generate flood-susceptibility maps using the optimized SVR model with the GOA and PSO algorithms. Finally, the predictive accuracy of the models was quantified using the statistical measures of root mean square error (RMSE), mean absolute error (MAE), and area under the receiver operating characteristic (ROC) curve (AUC). Although both the GOA and PSO algorithms improved SVR performance, the SVR-GOA model performed best (AUC = 0.959, RMSE = 0.31 and MSE = 0.098), followed by the SVR-PSO model (AUC = 0.959, RMSE = 0.33 and MSE = 0.11) and standalone SVR model (AUC = 0.87, RMSE = 0.35 and MSE = 0.12). Elevation, lithology and aspect had the highest IGR values and were identified as the most effective predictors of flood susceptibility.

Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB

Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naive Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data.

Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin, Egypt

Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in many countries require modeling of flood susceptibility to help in reducing the damages and impact of floods. The objective of the current work is to employ four data mining/machine learning models to generate flood susceptibility maps, namely boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA). This study was done in Wadi Qena Basin in Egypt. Flood inundated locations were determined and extracted from the interpretation of different datasets, including high-resolution satellite images (sentinel-2 and Astro digital) (after flood events), historical records, and intensive field works. In total, 342 flood inundated locations were mapped using ArcGIS 10.5, which separated into two groups; training (has 239 flood locations represents 70%) and validating (has 103 flood locations represents 30%), respectively. Nine themes of flood-influencing factors were prepared, including slope-angle, slope length, altitude, distance from main wadis, landuse/landcover, lithological units, curvature, slope-aspect, and topographic wetness index. The relationships between the flood-influencing factors and the flood inventory map were evaluated using the mentioned models (BRT, FDA, GLM, and MDA). The results were compared with flood inundating locations (validating flood sites), which were not used in constructing the models. The accuracy of the models was calculated through the success (training data) and prediction (validation data) rate curves according to the receiver operating characteristics (ROC) and the area under the curve (AUC). The results showed that the AUC for success and prediction rates are 0.783, 0.958, 0.816, 0.821 and 0.812, 0.856, 0.862, 0.769 for BRT, FDA, GLM, and MDA models, respectively. Subsequently, flood susceptibility maps were divided into five classes, including very low, low, moderate, high, and very high susceptibility. The results revealed that the BRT, FDA, GLM, and MDA models provide reasonable accuracy in flood susceptibility mapping. The produced susceptibility maps might be vitally important for future development activities in the area, especially in choosing new urban areas, infrastructural activities, and flood mitigation areas.

In flood susceptibility assessment, is it scientifically correct to represent flood events as a point vector format and create flood inventory map?

In this discussion article, we try to highlight and discuss the wrong way for representing an areal phenomenon “flood” as a point vector format in GIS-based flood susceptibility studies and creating what is called “flood inventory map”. Two examples from the literature were taken to show that a flood event cannot be represented by point except with very small map scales (1: 10000000) and this flood event should be with other flood events to form the “flood inventory map”. With the help of the other two examples from the previous studies, this article showed the wrong used way for representing flood worldwide and suggested an appropriate method for mapping flood susceptibility.

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for susceptibility mapping of the flash flood using the decision tree random subspace ensemble optimized by hybrid firefly–particle swarm optimization (HFPS), namely the HFPS-RSTree model. In this work, we used data from a flood inventory map consisting of 1866 polygons derived from Sentinel-1 C-band synthetic aperture radar (SAR) data and a field survey conducted in the northwest mountainous area of the Van Ban district, Lao Cai Province in Vietnam. A total of eleven flooding conditioning factors (soil type, geology, rainfall, river density, elevation, slope, aspect, topographic wetness index (TWI), normalized difference vegetation index (NDVI), plant curvature, and profile curvature) were used as explanatory variables. These indicators were compiled from a geological and mineral resources map, soil type map, and topographic map, ALOS PALSAR DEM 30 m, and Landsat-8 imagery. The HFPS-RSTree model was trained and verified using the inventory map and the eleven conditioning variables and then compared with four machine learning algorithms, i.e., the support vector machine (SVM), the random forests (RF), the C4.5 decision trees (C4.5 DT), and the logistic model trees (LMT) models. We employed a range of statistical standard metrics to assess the predictive performance of the proposed model. The results show that the HFPS-RSTree model had the best predictive performance and achieved better results than those of other benchmarks with the ability to predict flash flood, reaching an overall accuracy of over 90%. It can be concluded that the proposed approach provides new insights into flash flood prediction in mountainous regions.

Flood mapping using Sentinel-1 SAR Imagery: Case study of the November 2017 flood in Penang

The Sentinel-1 SAR image by the Europe Space Agency (ESA) in the Copernicus Programme is an open source data which offers good data to develop flood inundation mapping. The flood inundation mapping is essential information for local authority personals or researchers to develop flood inventory map. The objective of this study is to develop a flood inundation mapping on November 2017 event to assess its use as potential flood inventory mapping data in Penang. The threshold method was carried out using Sentinel-1 image to extract the flood map. This method is pixel value used to separate the grey image into two parts. The flooded and non-flooded areas can be identified by separating the backscatter lower than the threshold value. The results revealed that the flood inundation maps are able to be extracted out from Sentinel-1 images.

A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data

Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study proposed a novel modeling approach for spatial prediction of flash floods based on the tree intelligence-based CHAID (Chi-square Automatic Interaction Detector)random subspace, optimized by biogeography-based optimization (the CHAID-RS-BBO model), using remote sensing and geospatial data. In this proposed approach, a forest of tree intelligence was constructed through the random subspace ensemble, and, then, the swarm intelligence was employed to train and optimize the model. The Luc Yen district, located in the northwest mountainous area of Vietnam, was selected as a case study. For this circumstance, a flood inventory map with 1866 polygons for the district was prepared based on Sentinel-1 synthetic aperture radar (SAR) imagery and field surveys with handheld GPS. Then, a geospatial database with ten influencing variables (land use/land cover, soil type, lithology, river density, rainfall, topographic wetness index, elevation, slope, curvature, and aspect) was prepared. Using the inventory map and the ten explanatory variables, the CHAID-RS-BBO model was trained and verified. Various statistical metrics were used to assess the prediction capability of the proposed model. The results show that the proposed CHAID-RS-BBO model yielded the highest predictive performance, with an overall accuracy of 90% in predicting flash floods, and outperformed benchmarks (i.e., the CHAID, the J48-DT, the logistic regression, and the multilayer perception neural network (MLP-NN) models). We conclude that the proposed method can accurately estimate the spatial prediction of flash floods in tropical storm areas.

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.

Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin, Indo-Bangladesh Barind region

Flood is a natural but inevitable phenomenon occurring over the period of time. It not only damages the life, property and resources, but also hampers the economy of a nation. In this paper, an attempt has been made to delineate flood vulnerability areas for Kulik river basin through frequency ratio model. Parameters like slope, elevation, rainfall, drainage density, land use–land cover, TWI, population density, road density and household density were endorsed for understanding flood mechanism. In general, 70 (70%) flood locations from flood inventory map were randomly selected for constructing flood vulnerability map parameters and the rest 30 (30%) flood locations were used for justifying the outcomes. Flood vulnerability zone map was classified into five zones such as very low (2.02 km2), low (2.45 km2), moderately low (2.44 km2), high vulnerable (2.26 km2) and very high vulnerable (1.21 km2) area. As the AUC value for success rate is 0.901, the constructed flood vulnerable map with FR model is very much accurate for this river basin. The outcome of this paper will help the planners and decision-makers to take some probable measure to minimize flood vulnerability in this region.

Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods

Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial prediction of flood occurrence in the Quannan area, China. A flood inventory map with 363 flood locations was produced and partitioned into training and validation datasets through random selection with a ratio of 70/30. The spatial flood database was constructed using thirteen flood explanatory factors. The probability certainty factor (PCF) method was used to analyze the correlation between the factors and flood occurrences. Consequently, three flood susceptibility maps were produced using the NBTree, ADTree, and RF methods. Finally, the area under the curve (AUC) and statistical measures were used to validate the flood susceptibility models. The results indicated that the RF method is an efficient and reliable model in flood susceptibility assessment, with the highest AUC values, positive predictive rate, negative predictive rate, sensitivity, specificity, and accuracy for the training (0.951, 0.892, 0.941, 0.945, 0.886, and 0.915, respectively) and validation (0.925, 0.851, 0.938, 0.945, 0.835, and 0.890, respectively) datasets.

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.

Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework

Pluvial flooding is a common type of natural hazard caused by rainfall events with high intensity and short duration, which may lead to substantial property damages, transportation interruptions, and casualties. Modern cities are susceptible to pluvial flooding due to dense population and advanced economic development. To facilitate the development of better flood control and risk mitigation strategies, this study presents a new quantitative flood susceptibility analysis framework to estimate the potential flood extents and scale. The framework is based on the multi-criteria decision-making methods within a platform of the geographic information system (GIS). A composite urban flood risk index (FRI) is derived from various flood conditioning factors. The FRI consists of flood vulnerability index, hazard factors, and resilience capacity indicators. The flood-susceptible map is generated using the GIS spatial analysis tools and the analytic hierarchy process method. Zhengzhou city, China, is selected as the case study area. The result map shows that the highly susceptible areas are mainly located in Jinshui District, accounting for 64% of the total area of the risk zone. To further validate this framework, a flood inventory map is produced by mapping 74 test locations identified through survey data in this area, followed by plotting a receiver operating characteristic (ROC) curve. The ROC shows an area under the curve of 74.27%, which validates the proposed framework. Compared with other methods, the proposed framework is particularly suitable for application in data-scarce cases.

Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India

Flood is one of the most devastating natural calamities with environmental and socio-economic impacts. Comprehensive flood management is essential to reduce the flood effects on human lives and livelihoods. The main objective of this study was to examine the capability of geographic information system (GIS) in coupling with analytic hierarchy process (AHP) and frequency ratio (FR) model for flood vulnerability mapping. The study was carried out in two stages of analysis. First of all, a flood inventory map was prepared. Consequently, seven flood contributing factors viz. land elevation, slope angle, topographic wetness index, rainfall deviation, land use land cover, clay content in soil and distance from rivers were prepared for spatial analysis. Looking toward the flood records, a total of 270 flood points were marked from field study area, out of which 200 (75%) flood points were randomly selected for training data and the remaining 70 (25%) flood points were used for testing or validation purposes using prediction accuracy and success rate. The result revealed that distance from river, rainfall deviation and land use land cover have the great role for flood occurring in the study area with selected factor weight value (SFWV) of 0.33, 0.21 and 0.14, respectively. The validation result showed that prediction accuracy was 0.8142 and success rate was 0.8450 which may consider for validating the frequency ratio model that applied in present study. The application of AHP and frequency ratio model helps in identifying flood vulnerable zonation and potential risk area estimation. The findings from present study will be helpful for planner in flood mitigation strategies as a part of flood preparedness and will appear as a source for further research in the study area.

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines

Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human life. Modeling flood susceptibility in watersheds and reducing the damages caused by flooding is an important component of environmental and water management. The current study employs two new algorithms for the first time in flood susceptibility analysis, namely multivariate discriminant analysis (MDA), and classification and regression trees (CART), incorporated with a widely used algorithm, the support vector machine (SVM), to create a flood susceptibility map using an ensemble modeling approach. A flood susceptibility map was developed using these models along with a flood inventory map and flood conditioning factors (including altitude, slope, aspect, curvature, distance from river, topographic wetness index, drainage density, soil depth, soil hydrological groups, land use, and lithology). The case study area was the Khiyav-Chai watershed in Iran. To ensure a more accurate ensemble model, this study proposed a framework for flood susceptibility assessment where only those models with an accuracy of >80% were permissible for use in ensemble modeling. The relative importance of factors was determined using the Jackknife test. Results indicated that the MDA model had the highest predictive accuracy (89%), followed by the SVM (88%) and CART (0.83%) models. Sensitivity analysis showed that slope percent, drainage density, and distance from river were the most important factors in flood susceptibility mapping. The ensemble modeling approach indicated that residential areas at the outlet of the watershed were very susceptible to flooding, and that these areas should, therefore, be prioritized for the prevention and remediation of floods.

Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India

Flood is one of the most damaging catastrophic natural hazards affecting human lives in India. So, flood susceptibility mapping is essential for urban hydrology management. In this study, a Geographical Information System based-bivariate statistical analysis namely frequency ratio (FR) model was used to assess flood susceptibility of the middle and lower catchments of Subarnarekha River. The flood inventory map was made using field surveys and formal reports in the study area. In general, 32 flood locations (70%) that were inundated in June, 2008 was used for statistical analysis as training dataset, while the remaining 30% (14 flood locations) flooded areas were applied to validate the developed model. Eight flood conditioning factors namely elevation, slope, topographical wetness index, geomorphology, soil type, drainage, rainfall, and LULC (land use/land cover) were considered in this study. All these variables were resampled into 20 × 20 m pixel size. Each variable was classified using the quantile method and the FR probability model was used to evaluate the relationship of each class with flood occurrences. Finally, the flood susceptibility map was prepared and classified into very low, low, moderate, high, and very high susceptibility. Results of the built model was validated with the ground data (30% flood locations) using the area under the curves (AUC). The AUC for success rate was estimated as 84.80%, while the prediction rate was 81.20%. The produced flood susceptibility mapping using FR model could be important for researchers, planners, and local governments in order to flood mitigation strategies.