Flood Inventory Map Research Articles

Flood risk assessment using modern models integrated with GIS for susceptibility mapping of the M'zab Valley watershed (Algeria)

ABSTRACT Comprehensive flood control is needed because flooding can be catastrophic for people's lives and ways of living. This management must provide information on the hydrologic, geotechnical, environmental, social, and economic elements of floods. This work created maps for the flood susceptibility of the M'zab Valley using logistic regression (LR) and frequency ratio (FR). The main objective of the research was to strengthen the bivariate probability capabilities of FR and LR. A flood inventory map was created by extracting flood sites from multiple sources. The flood inventory was divided randomly into two parts: 30% was used for validation and the remaining 70% was used to train the models. Independent variable datasets included the distance from the river, rainfall, soil type, land use/land cover (LULC), elevation, drainage density, flow accumulation, and slope. The effect of each variable on flooding was assessed by comparing each independent variable with the dependent flood layer. The flood susceptibility map was assessed using the prediction rate approach on the validation dataset, which was not used for model construction. The accuracy assessment's findings revealed a prediction rate of FR 0.86 and LR 0.881, as well as a success rate of FR 0.924 and LR 0.924.

Flood Susceptibility Assessment of Chandpur Sadar Upazila using Analytical Hierarchy Process and Frequency Ratio Model

Flood susceptibility is a critical aspect of natural hazard assessment and disaster risk reduction. The objective of this study find out flood risk zone of chandpur sadar upazila using the Flood frequency ratio (FFR) and Analytical hierarchy process (AHP). The article explore the complex idea of flood susceptibility, focusing on analytical techniques used. The integration of twelve spatial data layers, such as Sediment Transport Index (STI), elevation, Topographic Wetness Index (TWI), slope, aspect, Land Use Land Cover (LULC), soil type, rainfall patterns, Normalize Difference Vegetation Index (NDVI), drainage density and distance from the river, is required for the assessment of flood vulnerability. A flood inventory map was buildup and using these methods, two vulnerability map was created, it was discovered that 60% & 48% of Chandpur's territory is categorized by very high to high risk zone. Flood has dangerous effect on livelihood. I was hoped that it will be a helpful study for researcher and people of this upazila will be benefited.

A novel machine learning tool for current and future flood susceptibility mapping by integrating remote sensing and geographic information systems

Flood mapping is essential for managing and mitigating the risks associated with flood events. This study integrates weighted overlay analysis and analytical network process to create a flood susceptibility map. Twelve factors were selected to assess flood risk in the region, and an analytical network process was used to derive weights of factors. Weighted overlay analysis was then used to apply weights to the flood conditioning factors to prepare the flood susceptibility map. The most prominent historical flood extent was derived by using Landsat 8, Sentinel-2 and Sentinel-1 satellite imageries, spectral indices and decision tree model to generate a flood inventory map. The flood susceptibility map was assessed for accuracy using the flood inventory map, achieving an overall accuracy rate of 88.3 %. The primary factors affecting flood risk were elevation, slope, and distance from the stream, with high-risk areas mainly located downstream. To develop the flood risk assessment model, the essential factors were classified into four main categories: topographical, environmental, hydrological, and anthropological. All possible input combinations were checked using the outlier robust extreme learning machine for one to twelve input variables, and the best model was selected using the outlier robust extreme learning machine with the input combination of five variables (i.e., normalizes difference vegetation index, geology, precipitation, elevation, and slope). Based on the Sixth Intergovernmental Panel on climate change report, the developed outlier robust extreme learning machine was used to project potential flood areas in the next 78 years. Based on the different scenarios, the results indicated that 10.93 %, 8.55 %, 8.09 %, 11.99 %, and 16.28 % of all areas are projected to be highly and very highly flood susceptible. The tools and methods outlined in this study can assist decision-makers in preparing for and responding to floods. Moreover, the method developed in this study can be implemented in watersheds and urban areas and help manage flood risk in urban drainage systems.

Flood susceptibility mapping using Sentinel 1 and frequency ratio technique in Jinjiram River watershed, India.

Assam is one of the most flood-prone states in India, and the state frequently experiences catastrophic floods that cause significant damage in terms of loss of life and property. Flood susceptibility is considered the most essential and crucial input for managing floodplains and fostering local and regional development. This study focuses on the generation of flood susceptibility maps using the Frequency Ratio (FR) technique and microwave remote sensing inputs in the Jinjiram watershed which experienced disastrous flooding in 2020. The study has been carried out by taking into consideration of different morphological, lithological, and hydrological factors. In this study the flood inventory map was created by extracting the time series SAR (Synthetic Aperture Radar) Sentinel 1 GRD (Ground Range Detected) images of flooded areas for the past 5years, from 2016 to 2020. A total of 72 inventory samples were identified of which 70% of total flooded samples were chosen for training and 30% for model testing at random basis. Applying these FR methods, the study determines a range of flood susceptibility which was then divided into five classes, from very low to very high. The Receiver Operating Characteristics (ROC) analysis was used to evaluate the accuracy of flood susceptibility maps generated using FR models. The AUC of ROC in flood susceptibility mapping is 0.81167 achieved, corresponding to a prediction accuracy of 81.17%. The findings can be used to calculate risk, develop flood control measures and infrastructural policies, and formulate sustainable water management policies for the watershed.

Application of Analytical Hierarchy Process and Frequency Ratio Model for Predictive Flood Susceptibility Mapping Using GIS for the Khazir River Basin, Northern Iraq

Flood damage assessment is considered the essential tool for evaluating risk to civil and agricultural systems in land use planning. The validity of the studies’ outcome depends on the availability of data and their spatial distribution. The present study came to compute flood susceptibility maps utilizing two application models: (i) the frequency ratio, and (ii) the analytical hierarchy process. These models were then tested in the Khazir River basin using GIS with a selection of twelve flood conditioning factors. The flood inventory variables layer and flood-causing factors were created using remote sensing data, a digital elevation model, and secondary data from various sources. Then, the flood inventory map was highlight divided into training and test data, with 105 flood sites (70%) used for training and 45 sites (30%) used for testing. After applying the areas under the curve for the frequency ratio and analytical hierarchy process models, which were 90.6% and 88.9%, respectively, the final flood sensitivity maps showed similar results for the two models, which confirm the effectiveness of the adopted methodology. The study found a considerable spatial variance in flood sensitivity maps, as (21.06%) of the flooded areas are classified as having very low sensitivity to flooding, (24.09%) are classified as having low vulnerability to floods, and (23.79%) are classified as having moderate vulnerability, (24.10%) classified as highly vulnerable to flooding, and (6.96%) classified as highly vulnerable to flooding. Flood danger ranged from very low in mountain locations to very high in plain areas close to the riverbanks. Obtained results could be improved if a land-use planning policy will be applied, in order to establish a master plan for water resources development to avoid flood damage.

Development of Flood-Hazard-Mapping Model Using Random Forest and Frequency Ratio in Sumedang Regency, West Java, Indonesia

Flooding, often triggered by heavy rainfall, is a common natural disaster in Indonesia, and is the third most common type of disaster in Sumedang Regency. Hence, flood-susceptibility mapping is essential for flood management. The primary challenge in this lies in the complex, non-linear relationships between indices and risk levels. To address this, the application of random forest (RF) and frequency ratio (FR) methods has been explored. Ten flood-conditioning factors were determined from the references: the distance from a river, elevation, geology, geomorphology, lithology, land use/land cover, rainfall, slope, soil type, and topographic wetness index (TWI). The 35 flood locations from the flood-inventory map were selected, and the remaining 18 flood locations were used for justifying the outcomes. The flooded areas from the RF model were 28.39%; the rest (71.61%) were non-flooded areas. Also, the flooded areas from the FR method were 8.02%, and the non-flooded areas were 91.98%. The AUC for both methods was a similar value – 83.0%. This result is quite accurate and can be used by policymakers to prevent and manage future flooding in the Sumedang area. These results can also be used as materials for updating existing flood-susceptibility maps.

Flood risk mapping of the flood-prone Rangpur division of Bangladesh using remote sensing and multi-criteria analysis

Identification of potential flood risk areas is crucial to reduce flood damage for the frequently flooded and low-lying South Asian developing countries. The present study has prepared district-level (District: second administrative unit of the country) flood risk map for the flood-prone Rangpur Division (Division: first administrative unit) of Bangladesh using the multi-criteria decision analysis along with the application of analytical hierarchy process (AHP) method. Eight physical factors such as elevation, slope, distance from river, drainage density, land cover, rainfall, height above nearest drainage (HAND), and topographic wetness index (TWI), and six social factors such as population density, dependent population, disabled population, female population, agriculture dependent population, and literacy have been assessed to create a final risk map. The flood risk map is divided into five risk zones: very low, low, moderate, high, and very high. Integration of the social factors along with the physical factors reflects the insight of the vulnerability and increases the authenticity of the generated risk map. This study found that 62.46% area of the Rangpur Division resides under the moderate to very high-risk zone of flooding. Using ROC (receiver operating characteristic)-AUC (area under the curve) curve, the risk map is validated with a score of 0.83 from the flood inventory map of 2020 generated from the Sentinel 1 image. This risk map will guide policymakers to easily identify the vulnerable area for flood hazards and suitable areas for development activities necessary to attain sustainable development.

Frequency Ratio Model as Tools for Flood Susceptibility Mapping in Urbanized Areas: A Case Study from Egypt

The occurrence of flash floods is a natural yet unavoidable occurrence over time. In addition to harming people, property, and resources, it also undermines a country’s economy. This paper attempts to identify areas of flood vulnerability using a frequency ratio approach. The frequency ratio (FR) model was used to produce flood prediction maps for New Cairo City, Egypt. Using field data and remote sensing data, 143 spatial flooded point sites were mapped to build a flood inventory map. The primary driving criteria for flash floods were determined to be elevation, slope, aspect, Land Use Land Cover (LULC), lithology, stream distance, stream density, topographic wetness index (TWI), surface runoff, and terrain ruggedness index (TRI), in that order of importance. A flood susceptibility map (FSM) has been created using the FR model, which combines geographical flooded sites and environmental variables. Our findings from FSM, roughly a fifth of the city is very highly susceptible to flooding (19.32%), while the remaining 40.09% and 13.14% of the study area rank very low and low risk, respectively. The receiver operating characteristic curve (ROC) technique was also used to validate the FSM, and the resulting results showed an area under the curve (AUC) of 90.11%. In conclusion, decision makers can employ models to extract and generate flood risk maps in order to better understand the effects of flash floods and to create alternative measures to prevent this hazard in similar regions. The results of this study will aid planners and decision makers in developing some likely actions to reduce floods vulnerability in this area.

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning – an updated version of machine learning techniques – and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.

Flood hazard assessment in Yemen using a novel hybrid approach of Grey Wolf and Levenberg Marquardt optimizers

This study aims to map flood susceptibility in the Qaa’Jahran watersheds located in Dhamar, Yemen, using geoprocessing and computational techniques. Historical flood data and SAR imagery were used to monitor and create a flood inventory map. The artificial neutral network (ANN) was trained using a novel algorithm called GWO_LM, which is a hybridization between the Levenberg-Marquardt algorithm (LM) and Grey Wolf Optimizer (GWO) meta-heuristic algorithm and compared the results with state of art machine learning algorithms. The GWO_LM_ANN model exhibited excellent performance in the evaluation, achieving a precision of 97.92%, sensitivity of 100%, specificity of 100%, F1 score of 98.95%, accuracy of 98.75% and AUC of 98.48. This indicates that using GWO_LM for training ANN enhanced the searching process for the optimal weights, resulting in outperforming other state-of-the-art models. The findings hold significant implications for disaster preparedness and response in the Qaa’Jahran watersheds, enabling targeted and efficient non-structural solutions to mitigate the detrimental effects of flash floods in particularly sensitive locations. The use of the previously unexplored GWO_LM model represents a notable advancement in flood susceptibility assessment, surpassing traditional methods and offering novel insights to the existing literature.

Spatial Flood Forecasting Modeling under Lack of Data Using RS and Optimized Support Vector Machine: A Case Study of the Zahedan Watershed

This research was conducted with the aim of producing flood susceptible area maps of the Zahedan catchment in Iran during lack of data using remote sensing. To determine the factors to be included in the model, factors identified in previous studies were prepared using remote sensing and were evaluated by the information gain ratio (IGR) method and the multicollinearity diagnostic test, and ultimately 10 factors with the highest IGR values were chosen as the most effective factors in the region. The flood inventory map was produced by processing the Sentinel-1 satellite data. The gathered data were used to map flood susceptibility maps with the SVM model optimized with IWO and ACO. The prediction accuracy of the models was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the area under the receiver operating characteristic (ROC) curve (AUC). While both optimization algorithms are effective in improving the performance of SVM, the hybrid of SVM with IWO shows the best performance in terms of statistical measures and Friedman test results. The results of the study confirm the good performance of the proposed models in spatial prediction of flood susceptible areas. Since over half of the urban lands of the city of Zahedan are at moderate to very high risk of flooding, this area needs more attention in terms of flood prevention and control measures.

A novel approach for flood hazard assessment using hybridized ensemble models and feature selection algorithms

Identifying flood-prone regions is critical for effective management of flood hazards as floods are among the most devastating natural disasters globally. However, accurate modeling and prediction of floods are challenging due to their complexity. The current research has proposed a novel approach for Flood Hazard (FH) prediction using hybrid Machine Learning (ML) models that integrate ensemble ML models with several Feature Selection (FS) algorithms. An optimum set of Flood Influential Factors (FIFs) was determined using the Simulated Annealing (SA) and Information Gain (IG) FS algorithms. The ensemble ML models employed include AdaboostM1 (ABM), Boosted Generalized Linear Model (BGLM), and Stochastic Gradient Boosting (SGB) algorithms. In addition, the hyper-parameters of the hybrid models were optimized using the random search (RS) method and repeated cross-validation technique. The proposed hybrid models were trained using flood inventory map and FIFs obtained from a spatial database. The results verified that the SA and IG algorithms detect 9 and 13 factors as FIFs in the FH assessment, respectively. Moreover, rainfall, distance to river, altitude, and lithology FIFs have a greater impact than the other factors in the Sardabroud watershed, Mazandaran Province, Iran. Several robust indicators, such as the area under curve (AUC) in relative operating characteristic (ROC) curves and statistical measurements, were employed to assess the robustness of hybrid models. SA-ABM model had the highest AUC value (0.983), while IG-ABM, SA-BGLM, SA-SGB, IG-BGLM, and IG-SGB had lower values (0.952 to 0.973). Finally, the SA-ABM hybrid model classified 27% of the study area as having a high hazard of floods.

Flood Susceptibility Assessment of Lagos State, Nigeria using Geographical Information System (GIS)-based Frequency Ratio Model

Flood is a common disaster affecting the lives and properties of humans. It has a history of causing great damage to infrastructure; disrupt transportation, also, a greater degree of flooding can lead to caving in of the earth causing landslides. Oftentimes, Lagos state, the economic capital of Nigeria has been subjected to flooding owing to heavy rainfall coupled with other causative factors. This study aims to prepare a flood susceptibility map of Lagos state using the frequency ratio model and Geographic Information System (GIS). In this paper, we have considered ten salient contributing factors to flooding, they are; slope, curvature, drainage proximity, drainage density, soil type, average annual rainfall, topographic wetness index, land use & land cover, normalized difference vegetation index, and elevation to delineate the area susceptible to flooding. The flood inventory map was prepared from 100 flood points identified from news reports, and Google Earth Imagery and was further divided into 70 for training and 30 for testing the model. The result shows that 12.54% and 11.62% of the total area of Lagos state have very high and very low levels of flood susceptibility, respectively. The Area Under the Curve has been used to validate the model and was found to perform satisfactorily with a success rate of 64% and a prediction rate of 61%. This work is a necessary input for mitigating flood hazards in the state and will serve a good purpose in making decisions for city planners and the government.

Flood susceptibility mapping by best-worst and logistic regression methods in Mersin, Turkey.

Flood disasters resulting from excessive water in stream beds inflict extensive damage. Floods are caused by the expansion of cities, the erosion of riverbeds, inadequate infrastructure, and increasing precipitation due to climate change. Floods cause great damage to agricultural areas and settlements. Regions that may be affected by floods should be identified, and precautions should be taken in these areas to prevent these damages. Flood susceptibility maps are produced for this reason. The purpose of this study was to construct a flood susceptibility map so that susceptible locations in Mersin may be identified. Firstly, 429 flood events were identified for the flood inventory map. Twelve conditioning factors, namely elevation, slope, distance to river, distance to drainage, drainage density, soil permeability, precipitation, land cover/land use, stream power index (SPI), topographic wetness index (TWI), aspect, and curvature were used to create flood susceptibility maps, applying logistic regression and best-worst methods. The flood inventory data were used to prepare susceptibility maps and test their consistency. The receiver operating characteristic (ROC) curve was used for consistency analysis. In logistic regression, 86% of floods were located within 20% of the study area that was categorized as high and very high susceptibility. According to the value of the area under the ROC curve (AUC), logistic regression had a 0.901 value. Land use, soil permeability, and elevation were the most important factors in the logistic regression method. In the best-worst method, 85% of floods were located within the 14% of the study area categorized as high and very high susceptibility. According to the AUC value, the best-worst method had a 0.898 value. Elevation, distance to river, and precipitation factors had the highest coefficient value in the best-worst method. Based on the AUC values, the flood susceptibility maps had a high prediction capacity.

Flood susceptible prediction through the use of geospatial variables and machine learning methods

Floods are one of the most perilous natural calamities that cause property destruction and endanger human life. The spatial patterns of flood susceptibility were assessed in this study using six applied machine learning (ML) models including Decision Tree (DT), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), Adaptive Boosting (AdaBoost), Logistic Regression (LR), and Support Vector Machines (SVM). A flood inventory map was provided using 180 flood points and 180 non-flood points. Both flood and non-flood locations were randomly partitioned into training and testing datasets with the ratio of 70:30. Sixteen various meteorological, hydrological, and geospatial variables were considered in the flood susceptibility assessment. The multi-collinearity analysis was performed to measure the intensity of correlations between the variables and determine whether factors can be included in the flood susceptibility assessment. From a total of sixteen variables, thirteen were chosen based on the multi-collinearity study. The five statistical indices Receiver Operating Characteristics curve (ROC), Jaccard index, F1 score, Overall Accuracy (OA), and Kappa coefficient (K) were used to evaluate models’ performance. Regarding the results of accuracy criteria, the RF model had the best predictive potential. Flood conditioning factors were reclassified using Quantile classification method and the importance of each class from a given flood conditioning factor was evaluated using the Frequency Ratio (FR) method. Additionally, the spatial relationship between the flood conditioning factors and the flood susceptibility map was investigated using the correlation matrix based on the Spearman correlation analysis. Findings of the correlation matrix revealed that the most important flood conditioning factors, in order of importance, were lithology, drainage density, distance from rivers, soil, rainfall, land cover, and Normalized Difference Moisture Index (NDMI). The flood susceptibility map, as the principal output of this study, was produced in five classes ranging from very low to very high susceptibility. The flood susceptibility map provided using the RF model (i.e., the model with the highest accuracy) indicated that the flood susceptibility is very high in 19.73% of the study area, high in 15.49%, moderate in 16.15%, low in 18.72%, and very low in 29.91%. Findings of this research might be an invaluable resource for better management of flooding since it specifically emphasized the primary causes of flooding as well as locations of heightened flood susceptibility.

Flood vulnerability mapping and urban sprawl suitability using FR, LR, and SVM models.

Floods are among the most destructive disasters because they cause immense damage to human life, property (land and buildings), and resources. They also slow down a country's economy. Due to the dynamic and complex nature of floods, it is difficult to predict the areas that are prone to flooding. In this study, an attempt was made to create a suitability map for future urban development based on flood vulnerability maps for the catchment area of Taif, Saudi Arabia. Three models were used for this purpose, including bivariate (FR), multivariate (LR), and machine learning (SVM) were used. Thirteen parameters were used as flood-contributing parameters. The inventory map was constructed using field surveys, historical data, analysis of RADAR (Sentinel-1A), and Google Earth imagery collected between 2013 and 2020. In general, 70% flood locations were randomly selected from the flood inventory map to generate the flood susceptibility model, and the remaining 30% of the flood locations were used for model validation. The flood susceptibility map was classified into five zones: very low, low, moderate, high, and very high. The AUC value used to predict the performance of the models showed that the accuracy reached 89.5, 92.0, and 96.2% for the models FR, LR, and SVM, respectively. Accordingly, the flood susceptibility map produced by the SVM model is accurate and was used to produce a flood vulnerability map with the help of urban and road density maps. Then slope and elevation maps were integrated with the flood vulnerability model to produce the final suitability map, which was classified into three zones: isolated zone, low suitability, and high suitability areas. The results showed that the highly suitable areas are located in the east and northeast of the Taif Basin, where the flood risk is low and very low. The results of this work will improve the land use planning of engineers and authorities and take possible measures to reduce the flood hazards in the area.

Hybrid approach for flood susceptibility assessment in a flood-prone mountainous catchment in China

• We developed a hybrid approach integrating physical-based flood models with random forest for flood susceptibility assessment. • We identified the relatively significant flood-inducing factors. • The geographical and hydromorphological factors had greater effects on riverine flooding. • This flood susceptibility assessment is suitable in ungauged areas. Flooding is one of the most destructive natural hazards that has caused catastrophic effects worldwide. Recently, machine learning methods have become widespread in flood susceptibility assessments due to their efficiency and robustness. However, flood inventory data acquired from historical flooding records and field surveys in existing studies are sparse and not enough to develop accurate machine learning models. Thus, we developed a hybrid approach that integrates hydrodynamic model (HEC-HMS/RAS), rapid flood model (height above the nearest drainage, HAND) and machine learning model (random forest, RF) for flood susceptibility assessment. The proposed approach was then implemented in the Xinluo watershed, a flood-prone mountainous catchment in China. The results showed that the spatially continuous flood inventory map generated from HEC-HMS/RAS model satisfies the big data requirements of the RF model, and the proposed hybrid approach demonstrates the effectiveness and efficiency in flood susceptibility assessment with overall accuracy and area under the ROC curve values of 0.915 and 0.912, respectively. The geographical and hydromorphological factors had greater effects on riverine flooding with average contribution of 27.8% and 27.6%. In comparison, the average contribution of land cover and meteorological factors were 24.3% and 20.3%, respectively. The method proposed in this study is effective in assessing and forecasting flood susceptibility and can facilitate resilience planning as well as integrated flood management.

Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability.

Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt.

Floods are among the most devastating environmental hazards that directly and indirectly affect people's lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

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.