Flood Susceptibility Assessment Research Articles

Flood susceptibility assessment of Jhelum River Basin: A comparative study of TOPSIS, VIKOR and EDAS methods

Floods are frequent natural hazards that cause widespread destruction, particularly in low-elevated areas. This study focuses on identifying flood susceptible zones in the Kashmir Valley, known for historical flooding attributed to the overflow of the Jhelum River. Various Multi-Criteria Decision Making (MCDM) techniques, including Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijai Compromission Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS), were employed in this research. A total of 17 multidimensional factors were considered, and multicollinearity tests revealed no correlation among these factors. The results of the MCDM models indicate that areas along the Jhelum River are classified under very high flood susceptible zone. Specifically, Srinagar city is consistently classified under very high flood susceptible zone by all three models. Approximately 4.27%, 9.67%, and 5.39% of the total area were identified as very high susceptible areas by TOPSIS, VIKOR, and EDAS, respectively. The models exhibited robust performance, as evidenced by the Area Under the Curve of the Receiver Operating Characteristics curve (AUC-ROC). Notably, VIKOR demonstrated excellent performance among the three models in generating flood susceptible maps. The favorable outcomes of these models underscore their potential application in similar regions facing comparable challenges. This study carries significant implications for policymakers, administrators, and local authorities involved in flood management within the Kashmir Valley. The insights provided can inform proactive measures and strategies to mitigate the impact of floods and enhance the overall resilience of the region.

Coastal flood susceptibility assessment along the Northern coast of Portugal

Assessing the susceptibility of sandy beaches to flooding during major storms is an important topic, given the implications that these events can have for beach-dependent coastal systems. A flood susceptibility index based on wave run-up, storm surge and beach morphology is applied to three sectors of the northwest coast of Portugal. An extended ERA5 wave dataset is used (1950–2022) to characterize the most frequent storm events, and topographic data from aerial UAV surveys are examined to obtain specific morphological parameters of the sandy beaches – mean face slope and height of the beach. The flood susceptibility index results show that 54 % of the analyzed coast is classified with extreme or very high susceptibility. The most critical sector is the coastal stretch between Cortegaça and Torrão do Lameiro beaches, where mostly natural systems are present, while the urban front of Furadouro is frequently flooded during storm events. Between Miramar beach (south) and Paramos beach, with a susceptibility index classified between medium and extreme, the presence of some urban fronts points to high-risk exposure situations. The sector between São Jacinto and Poço da Cruz beaches presented lower susceptibility levels for the three urban areas. The problem does not end with the threat to urban fronts, which already have several defense structures, but it is also extends to dune systems and sand dykes.

Flood susceptibility modeling by integrating tree‐based regression with metaheuristic algorithm, BWO

AbstractFloods are becoming more widely acknowledged as a common occurrence of nature's dangers on a global scale. Although forecasting models primarily focus on timely warnings, models aimed at evaluating dangerous zones can play a vital role in shaping policies for adaptation, mitigation, and reducing the risk of disasters. Using machine learning techniques including hybrid black widow optimization (BWO) with XGBoost, LGBoost, and AdaBoost. We generate a flood susceptibility map for considered region of lower mahanadi basin (LMB). This study examines the effectiveness of these machine learning models in assessing and mapping flood susceptibility, while also providing suggestions for future research in this area. Flood susceptibility model was developed using 13 variables: Altitude, Aspect, Curvature, Distance from river, Drainage Density, Stream Power Index (SPI), Sediment Transport Index (STI), Rainfall intensity, Land Use Land Cover (LULC), Topographic Wetness Index (TWI), Terrain Roughness Index (TRI), Normalized Difference Vegetation Index (NDVI), and slope. Additionally, flood inventory data were incorporated into the model. Dataset was divided into a 70% portion for training model and a 30% portion for validating model. To assess the performance of the model, several evaluation metrics were employed, including receiver operating characteristic (ROC) curve and other performance indices. Evaluation of flood susceptibility mapping, using ROC curve method in combination with flood density yielded strong and reliable results for various models. BWO‐XGBoost achieved a score of 0.889, BWO‐LGBoost achieved a score of 0.937, and BWO‐ADABoost achieved a score of 0.904. These scores indicate effectiveness of these models in accurately predicting flood susceptibility in the study area. A comparison was made with commonly used methods in flood susceptibility assessment to evaluate the efficiency of proposed models. It was found that having a first‐class and enlightening database is crucial for accurately classifying flood types in flood susceptibility mapping. This aspect greatly contributes to improving the overall performance of the model. Among the evaluated methods, the hybrid model BWO‐LGBoost demonstrated better performance compared with others, indicating its effectiveness in accurately predicting flood susceptibility.

Evaluation of sediment budgets in catchments prone to flash flood-related debris flows: A case study from the southern Apennines (Italy)

The evaluation of sediment budgets of flash-flood prone torrential catchments is important for the assessment of flood susceptibility and hazard in piedmont areas. Here, we developed and tested a predictive, quantitative geomorphological technique for estimating the magnitude of debris flows related to flash floods triggered by extreme rainfall events. We applied our method to steep torrential catchments that allowed testing of our technique through quantification of the effects of recently-occurred flash floods. The three study catchments are located in southern Italy and experienced flash flood-related debris flows triggered by a rainstorm with a recurrence interval > 200 yrs. The study basins are small size, steep, narrow and the stream network has a low order (maximum 3rd order). We estimated the volumes of loose sediment stored in the bottoms of the mainstem valleys of the investigated mountainous catchments prior to the flash flood and compared our results with the sediment volumes entrained by the debris flows. The latter volumes were estimated by post-event data collected by means of field surveys. The results were mutually consistent so supporting our approach. The approach is fit to the analysis of torrential catchments with geomorphological features comparable to those of our study basins, provided (i) the stream is confined by hard rock slopes not affected by channel lateral erosion, (ii) all stored sediment is eroded and transported downstream during the flood, and (iii) no landslide occurs along the hillslopes. For these types of ungauged catchments, our method can be applied extensively, even in the absence of field data and measurements, for flash flood hazard assessment and planning of mitigation strategies.

Optimizing flood susceptibility assessment in semi-arid regions using ensemble algorithms: a case study of Moroccan High Atlas

This study explores and compares the predictive capabilities of various ensemble algorithms, including SVM, KNN, RF, XGBoost, ANN, DT, and LR, for assessing flood susceptibility (FS) in the Houz plain of the Moroccan High Atlas. The inventory map of past flooding was prepared using binary data from 2012 events, where “1” indicates a flood-prone area and “0” a non-flood-prone or extremely low area, with 762 indicating flood-prone areas. 15 different categorical factors were determined and selected based on importance and multicollinearity tests, including slope, elevation, Normalized Difference Vegetation Index, Terrain Ruggedness Index, Stream Power Index, Land Use and Land Cover, curvature plane, curvature profile, aspect, flow accumulation, Topographic Position Index, soil type, Hydrologic Soil Group, distance from river and rainfall. Predicted FS maps for the Tensift watershed show that, only 10.75% of the mean surface area was predicted as very high risk, and 19% and 38% were estimated as low and very low risk, respectively. Similarly, the Haouz plain, exhibited an average surface area of 21.76% for very-high-risk zones, and 18.88% and 18.18% for low- and very-low-risk zones respectively. The applied algorithms met validation standards, with an average area under the curve of 0.93 and 0.91 for the learning and validation stages, respectively. Model performance analysis identified the XGBoost model as the best algorithm for flood zone mapping. This study provides effective decision-support tools for land-use planning and flood risk reduction, across globe at semi-arid regions.

A novel framework for feature simplification and selection in flood susceptibility assessment based on machine learning

Study regionYangtze River Delta core urban agglomeration, China Study focusTraditional research on flood susceptibility assessment using machine learning often seeks to enhance model performance by increasing the number of input variables, which is impractical in regions with limited data availability. In this study, we constructed a variable system comprising 13 features for flood susceptibility assessment through machine learning techniques. A flexible framework, primarily incorporating methods for importance value calculation and repeated random sampling, were established to identify a minimal set of features that yield high-performance classifiers. Finally, the feasibility of the proposed framework was verified by comparing the classifier performances and flood susceptibility maps. New hydrological insights for the regionResults underscored the significance of features such as Land Use / Land Cover, Impervious Area, Normalized Difference Vegetation Index, Distance to Lake and Built-up Probability in model development. These five features proved sufficient to produce a classifier with Area Under the Curve (AUC) indices exceeding 0.9 for both training and testing data. Susceptibility maps generated using varying feature counts revealed that regions with limited vegetation cover and near lakes face higher flood susceptibility. The framework's feasibility and viability were confirmed by the excellent classifier performance (mean AUC > 0.9) with reduced features and the consistent outcomes of generated maps, offering theoretical and technical support for flooding research in data-constrained regions.

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.

Spatial Assessment of Flood Susceptibility in Assam, India: A Comparative Study of Frequency Ratio and Shannon’s Entropy Models

Spatial Assessment of Flood Susceptibility in Assam, India: A Comparative Study of Frequency Ratio and Shannon’s Entropy Models

Integrated analytic hierarchy process and fuzzy analytic hierarchy process for Sahel watershed flood susceptibility assessment, Algeria

Abstract Effective disaster management heavily relies on accurate flood susceptibility mapping. The fuzzy analytic hierarchy process (FAHP) is adept at considering the imprecise nature of decision-making criteria. This study assesses FAHP's effectiveness in flood susceptibility mapping, comparing it with the conventional analytic hierarchy process (AHP). By using Geographic Information System-analyzed remotely sensed data, the research systematically evaluates flood risk southeast of Algiers. Various datasets, including Digital Elevation Model, slope, precipitation, and land use maps, were collected via remote sensing. A linear fuzzy membership function transformed the data into fuzzy values. AHP determined the importance of each dataset, with calculated weights multiplied by corresponding fuzzy values. Fuzzy analysis combined these characteristics into a five-category flood risk map, verified with Google Earth and satellite images. Results indicate a high potential for flood hazard mapping, categorizing 30% of frequently flooded regions as high risk. Maps reveal north basin areas are more flood prone due to excessive precipitation, and urban areas in floodplains are vulnerable. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) assessments demonstrate AHP and FAHP's effectiveness. AUC values of 88.40 and 92% indicate that both models accurately predict flood-prone areas. FAHP excels, reducing subjectivity and ambiguity in human judgments.

Flood Susceptibility Assessment with Random Sampling Strategy in Ensemble Learning (RF and XGBoost)

Due to the complex interaction of urban and mountainous floods, assessing flood susceptibility in mountainous urban areas presents a challenging task in environmental research and risk analysis. Data-driven machine learning methods can evaluate flood susceptibility in mountainous urban areas lacking essential hydrological data, utilizing remote sensing data and limited historical inundation records. In this study, two ensemble learning algorithms, Random Forest (RF) and XGBoost, were adopted to assess the flood susceptibility of Kunming, a typical mountainous urban area prone to severe flood disasters. A flood inventory was created using flood observations from 2018 to 2022. The spatial database included 10 explanatory factors, encompassing climatic, geomorphic, and anthropogenic factors. Artificial Neural Network (ANN) and Support Vector Machine (SVM) were selected for model comparison. To minimize the influence of expert opinions on model training, this study employed a strategy of uniformly random sampling in historically non-flooded areas for negative sample selection. The results demonstrated that (1) ensemble learning algorithms offer higher accuracy than other machine learning methods, with RF achieving the highest accuracy, evidenced by an area under the curve (AUC) of 0.87, followed by XGBoost at 0.84, surpassing both ANN (0.83) and SVM (0.82); (2) the interpretability of ensemble learning highlighted the differences in the potential distribution of the training data’s positive and negative samples. Feature importance in ensemble learning can be utilized to minimize human bias in the collection of flooded-site samples, more targeted flood susceptibility maps of the study area’s road network were obtained; and (3) ensemble learning algorithms exhibited greater stability and robustness in datasets with varied negative samples, as evidenced by their performance in F1-Score, Kappa, and AUC metrics. This paper further substantiates the superiority of ensemble learning in flood susceptibility assessment tasks from the perspectives of accuracy, interpretability, and robustness, enhances the understanding of the impact of negative samples on such assessments, and optimizes the specific process for urban flood susceptibility assessment using data-driven methods.

Flood susceptibility assessment using machine learning approach in the Mohana-Khutiya River of Nepal

Nepal, known for its challenging topography and fragile geology is confronted with the constant threat of floods leading to substantial socio-economic losses annually. However, the country's efforts in planning and managing flood risks remain insufficient, especially in the vulnerable Mohana-Khutiya River. Therefore, this study focused on the Mohana-Khutiya River and utilizes the Maximum Entropy (MaxEnt) model to comprehensively map flood susceptibility and fill crucial gaps in flood risk assessments. This study employed a combination of 10 geospatial environmental layers and field-based past flood inventory to implement the MaxEnt machine learning model for flood susceptibility modeling. The available past flood data were divided into two sets, with 75% allocated for model construction and the remaining 25% for model validation. This study demonstrated that the proximity of the river had a significant impact (33.1%) on the occurrence of the flood. Surprisingly, the amount of annual precipitation throughout the year exhibited no detectable contribution to the flood event in the study site. About 4.9% area came under the high flood susceptible zone followed by 12.75 % in the moderate zone and 82.34% in the low-risk zone. The model exhibited excellent performance with an Area Under Curve (AUC) value of 0.935 and a low standard deviation of 0.018, indicating accurate predictions and consistent precision. These results highlight the model's reliability and its significance for developing disaster management policy by local government in the study site. Future research should refine the MaxEnt model by including more variables, validating against observed flood events, and exploring integration with other flood modeling approaches.

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm.

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

Local-scale flash flood susceptibility assessment in northeastern Bangladesh using machine learning algorithms

The recurring flash floods in northeastern Bangladesh have been causing significant damage to the lives and livelihoods of local people every year, underscoring the necessity of effective flood management and mitigation efforts. Identifying flood hazard areas is the very first step in achieving such ends. While machine learning algorithms have been widely used in flood susceptibility studies, there is a significant dearth of research addressing their application for local-scale assessment of flood susceptibility in Bangladesh. Besides, in most of the flood susceptibility studies, only statistical metrics are used to evaluate the prediction capability of machine learning algorithms, overlooking their ability to account for spatial consistency. Therefore, this study attempts to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) algorithms in flash flood susceptibility assessment at local scale using statistical and spatial metrics. RF model performed better than SVM with area under the curve (0.964), accuracy (92.1 %) and kappa (0.894). Five susceptibility zones were identified using a natural breaks method: very high, high, moderate, low and very low. The overall spatial agreement between the susceptibility maps of RF and SVM was 73.3 %. For very low susceptible classes, the spatial agreement was highest for RF-SVM (97.15 %) and SVM-RF (81.54 %). The findings of this study can be useful for a more accurate assessment of flash flood susceptibility in similar local settings. Moreover, the prepared susceptibility map can be helpful for taking sustainable measures to mitigate the devastating effects of flood hazard in northeastern Bangladesh.

Flood Susceptibility Assessment through Statistical Models and HEC-RAS Analysis for Sustainable Management in Essaouira Province, Morocco

Floods are natural disasters that often impact communities living in low-lying areas in the northern and central parts of Morocco. In this study, our aim was to create a flood susceptibility map using three methods; the hierarchy process (AHP) frequency ratio model (FR) and the weights of evidence (WoE) model. We extensively examined the area identified by these approaches using a hydraulic analysis software called HEC-RAS (version 6.3.1). Our analysis focused on the Essaouira watersheds in Morocco, where we identified around 197 flood locations. Out of these, we randomly selected 70% for modeling purposes while the remaining 30% were used for validation. Ten factors that influence floods were considered, such as slope, elevation, proximity to rivers, drainage density, stream order, land use patterns, rainfall data, lithology (permeability level) index (TWI), and curvature. We obtained these factors from data sources. Finally, we generated a flood susceptibility map and evaluated its accuracy by calculating the area under the curve (AUC). The validation results confirmed that all three models were robust and effective with an AUC of 90. Moreover, the research uncovered a trend of vulnerability with the most susceptible area being in close proximity to the city of Essaouira along the Oued Ksob. A detailed analysis using HEC-RAS was conducted at this identified location, pinpointing the village of Diabat as highly exposed. These findings hold significance for flood management, empowering decision makers, scholars, and urban planners to make informed choices and implement strategies that can minimize the impact of floods in susceptible regions while minimizing potential damages.

An assessment of flash flood susceptibility in Golestan province, Iran, using multiple computational approaches

Purpose Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours. Design/methodology/approach This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed. Findings Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk. Originality/value The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

Assessment of urban flood susceptibility and role of urban green space (UGS) on flooding susceptibility using GIS-based probabilistic models.

With rapid urbanization, the green space in urban areas is replaced with impervious built-up areas, which increases the frequency of urban floods. Kamrup Metropolitan District, Assam, is near the Brahmaputra and is highly prone to urban flooding. The present study aims to develop the urban flood susceptibility index (FSI) and to analyze the role of urban green space (UGS) as a nature-based solution (NBS) for urban flood susceptibility. Two types of flooded urban areas are observed using a two-stage cluster analysis. A GIS-based urban FSI is developed using logistic regression (LR), frequency ratio (FR), Shannon entropy (SE), certainty factor (CF), and weight of evidence (WoE) models, and variation of FSI is assessed for different UGS areas. According to the area under curve (AUC), the performance of all five models falls under the good to excellent class. The average UGS ratio for non-flooded is higher than for flooded areas, and with an increase in the area of UGS, the flooding probability decreases for all the models. The findings of the present study emphasize the importance of UGS and can be used for effective urban flood risk mitigation and management planning.

Flood Susceptibility Assessment through Hydromorphometric Analysis of Esmahanım River Basin

Çalışma alanını oluşturan Esmahanım Deresi havzası Düzce’nin Akçakoca, Cumayeri ve Çilimli ilçeleri sınırları içerisinde yer almaktadır. Söz konusu çalışma alanında çeşitli tarihlerde meydana gelen taşkınlarda maddi ve manevi zararlar oluşmuştur. Bu çalışmanın amacı hidromorfometrik analizlerle Esmahanım Deresi havzası ve alt havzalarına ait taşkın duyarlılık sınıflandırmasının yapılmasıdır. Çalışmada yersel çözünürlüğü 12,5 m. olan 2008 tarihli ALOS (PALSAR) sayısal yükselti verisinden elde edilen Sayısal Yükselti Modeli (SYM) verisi kullanılmıştır. Analizlerin yapılmasında 
 Arc-GIS 10.5 yazılımı Mekansal Analiz araç kutusu içinde yer alan Hidroloji aracından yararlanılmıştır. Esmahanım Deresi alt havzası, Gübi alt havzası ve Küpler alt havzasından oluşan 3 alt havza çeşitli hidromorfometrik analizlere tabi tutulmuştur. Analiz sonuçlarına göre alt havzalar kendi aralarında taşkın duyarlılığı bakımından kıyaslanmışlardır. Elde edilen sonuçlara göre Gübi alt havzasında taşkın duyarlılığı yüksek iken, Esmahanım alt havzasında orta, Küpler alt havzasında ise taşkın duyarlılığı düşük çıkmıştır. Taşkın duyarlılığının yüksek ve orta olduğu Esmahanım ve Gübi alt havzalarında planlanan beşeri faaliyetlerde bu durumun göz önüne alınarak uygulamaların yapılması önem arz etmektedir.

Flood Susceptibility Assessment and its Management in Malda District of West Bengal through Geospatial Technique

Flood is one of the most popular natural hazards that create havoc in the society. Flood involves inundation of a huge tract of area under water owing to heavy rainfall. Occurrence of flood lays a severe impact upon the agricultural sector. Flood not only brings about destruction of crops but also brings down the productivity of the soil. Malda district of West Bengal is not an exception in this regard. The district is one of the most flood prone districts and involves a lot of loss in crop production. The present study aims to identify the flood susceptible zones of the district through the application of Analytical Hierarchy Process and geospatial techniques and also makes an attempt to detect how much cropland falls within the very high susceptible flood prone area so that proper precautionary measures can be taken to minimize the maximum damage. The study results reveal that the western part of the district is more flood prone and about 55.27% of very high flood prone area is dominated by croplands. Hence, proper flood management techniques have to be adopted in this area to reduce the loss of production of crops owing to floods.

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.

An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge

Floods are one of the most common natural disasters, resulting in the extensive destruction of infrastructure, property, and human life. The destructive potential of a flood depends on numerous factors, including the size of the flood, the rate of flooding, the time it takes for the water to move through a given area, the river's planform and cross-section geometry, and other similar factors. The present study is a unique analysis of flood mapping that was accomplished with the help of the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and hydrogeomorphic response to floods by integrating geospatial analysis and unit stream power modeling. The Indus catchment region of Pakistan is where the subject topic is put into practice. According to the hydrologic analysis of the yearly peak discharge, the hydro-station in Gilgit-Baltistan can move boulders measuring up to 0.5 m in height during significant flooding. On the other hand, there will be no change to the geometry of the cross-section throughout 1980–2020 in Gilgit-Baltistan. The flood susceptibility map is constructed using data from twelve influencing parameters, including elevation, proximity to the drainage network, slope, drainage density, geomorphology, rainfall, the curvature of the topography, flow accumulation, geology, land use, Topographic Wetness Index (TWI), and Stream Power Index (SPI). The area under the curve (AUC) approach, which demonstrates a substantial degree of accuracy (85% and 83%), is utilized to evaluate the effectiveness of the AHP and FR. The current study fills the gaps between the geospatial approach and the hydrogeomorphic assessment of flood to determine flood susceptibility.