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The main purpose of this study is to detect flash-flood potential areas based on the pre-event characteristics in the study area. Five physical factors including slope, land use, soil texture, tree canopy density, and drainage density were used to create index maps in the Geographic Information System (GIS) environment. Each index was classified from 1 to 10 by identifying their influence levels with the presence or absence of flash floods based on the Flash Flood Potential Index (FFPI) model. As the result, the most susceptible areas were given a value of 10 and the least susceptible areas were given a value of 1. These indices were then mapped and integrated into a weighted linear model. Analytic Hierarchy Process (AHP) was used to determine the weighted correlation among elements based on their importance to this phenomenon. Weighted Flash Flood Potential Index (WFFPI) map was classified into four levels, including very high, high, moderate, and low flash flood potential. The result shows that more than 10% of the total area has the greater possibility to be affected by a high-intensity flood, and distributed at the north and northeast mountain ranges while most of the area is at a medium value with 84.35%. Finally, the similarity between results in this study and reference results from HEC-RAS model showed the reliability of the applied method.

Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.

There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

The importance of identifying the areas vulnerable for both floods and flash-floods is an important component of risk management. The assessment of vulnerable areas is a major challenge in the scientific world. The aim of this study is to provide a methodology-oriented study of how to identify the areas vulnerable to floods and flash-floods in the Buzău river catchment by computing two indices: the Flash-Flood Potential Index (FFPI) for the mountainous and the Sub-Carpathian areas, and the Flood Potential Index (FPI) for the low-altitude areas, using the frequency ratio (FR), a bivariate statistical model, the Multilayer Perceptron Neural Networks (MLP), and the ensemble model MLP–FR. A database containing historical flood locations (168 flood locations) and the areas with torrentiality (172 locations with torrentiality) was created and used to train and test the models. The resulting models were computed using GIS techniques, thus resulting the flood and flash-flood vulnerability maps. The results show that the MLP–FR hybrid model had the most performance. The use of the two indices represents a preliminary step in creating flood vulnerability maps, which could represent an important tool for local authorities and a support for flood risk management policies.

The purpose of this paper is to identify areas with high flash-flood potential based on an evaluation of physiographic factors controlling the formation of surface runoff. The research method relies on the use of the Flash Flood Potential Index (FFPI), which incorporates physiographic characteristics from the catchment (terrain slope, profile curvature, land use and soil texture). The spatial distribution of the physiographic factors (which contribute to the creation, control and concentration within the drainage network of the overland flow) and the classified zoning of areas according to their hydrological response were achieved with GIS techniques. The results obtained show that physiographic factors on 227 sq km (29%) favor surface runoff on slopes and its localization towards the drainage network. Notably, the highest values of FFPI belong to the lower part of the catchment, where high human population density can be found, reflecting an increased vulnerability to floods and inundations of this area.

Given the correlation between deforestation and floods and the many negative consequences of these two factors, this paper aims to identify the areas vulnerable to the risks of flash flooding and the influence of deforestation on this phenomenon based on the flood recordings from 2005 to 2016 and the forest changes from 2000 to 2016. The high frequency of flooding events is affecting the economic development of the area, yearly almost half of the territorial-administrative units that overlap the area of the basin are hit by floods. The methodology was applied for the Moldova river basin, located in the northern part of Romania by computing the Flash Flood Potential Index (FFPI). The results are used to determine the flash flood potential and identify how the loss of forest affected the territorial-administrative units with the most frequent flooding events.

The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.

Yunnan Province, located in Southwest China, suffers from massive flash flood hazards due to its complex mountainous hydrometeorology. However, traditional flash flood forecasting approaches can hardly provide an effective and comprehensive guide. Aiming to build a multilevel guidance system of flash flood warning for Yunnan, this study develops a Cascading Flash Flood Guidance (CFFG) system, progressively from the Flash Flood Potential Index (FFPI), the Flash Flood Hazard Index (FFHI) to the Flash Flood Risk Index (FFRI). First, land cover and vegetation cover data from MODIS products, the Harmonized World Soil Database soil map, and SRTM slope data are used in generating a composite FFPI map. In this process, an integrated approach of the analytic hierarchy process and the information entropy theory is used as a weighting method. Then, three standardized rainfall amounts (average daily amount in flood seasons, maximum 6 h and maximum 24 h amount) are added to derive FFHI. Further inclusion of GDP, population and flood prevention measures as vulnerability factors for the FFRI enabled prediction of the flash flood risk. The spatial patterns of the CFFG indices indicate that counties in east Yunnan are most susceptible to flash floods, which agrees with the distribution of observed flash flood events. Compared to other approaches, the CFFG system could be a useful prototype in mapping characteristics of China’s flash floods in a cascading manner (i.e., potential, hazard and risk) for users at different administrative levels (e.g., town, county, province and even nation).

Given that floods continue to cause yearly significant worldwide human and material damages, flood risk mitigation is a key issue and a permanent challenge in developing policies and strategies at various spatial scales. Therefore, a basic phase is elaborating hazard and flood risk maps, documents which are an essential support for flood risk management. The aim of this paper is to develop an approach that allows for the identification of flash-flood and flood-prone susceptible areas based on computing and mapping of two indices: FFPI (Flash-Flood Potential Index) and FPI (Flooding Potential Index). These indices are obtained by integrating in a GIS environment several geographical variables which control runoff (in the case of the FFPI) and favour flooding (in the case of the FPI). The methodology was applied in the upper (mountainous) and middle (hilly) catchment of the Prahova River, a densely populated and socioeconomically well-developed area which has been affected repeatedly by water-related hazards over the past decades. The resulting maps showing the spatialization of the FFPI and FPI allow for the identification of areas with high susceptibility to flash-floods and flooding. This approach can provide useful mapped information, especially for areas (generally large) where there are no flood/hazard risk maps. Moreover, the FFPI and FPI maps can constitute a preliminary step for flood risk and vulnerability assessment.

A large debris flow triggered by a rock avalanche in the Raunthi glaciated valley resulted in flash floods in the Rishiganga and Dhauliganga rivers on 07 February 2021 in Uttarakhand, India. Hydel projects, houses, roads and bridges in the path of debris flow were damaged resulting in many deaths. We have used high-resolution satellite data (e.g. Pleiades, WorldView, Kompsat, Cartosat, Resourcesat, Planet) to study the source of flash floods and cause of the slope failure. Our detailed geological assessment, carried out using stereoscopic Pleiades images (50 cm), revealed rock avalanche as the main source of slope failure. The slope failure has exposed a ~197-m-high head scarp near the crown and is controlled by two sets of joints and a foliation that helped in the wedge type failure. The volume of failed mass (rock and ice) estimated by cut and fill method using digital elevation models (DEMs) is ~ 29.3 million m3. The rock and ice descended from a height of ~5474 m and then crashed onto the moraine and ice bridges present in the valley at ~3732 m after travelling ~2.9 km along a steep slope. The heat generated by friction during run out and conversion of potential energy to kinetic energy due to the crashing on valley floor melted snow and ice. The ice melt water along with enhanced snow melting due to high ambient temperature on that day got intermixed with debris and created a slush, which was mobilised as a channelised flash flood. Multi-temporal high-resolution satellite data analysis showed that the debris flow was initiated at ~10:08:45 h (IST), and it travelled with a velocity of ~10.6 m/s. The rock avalanche event lasted for ~50 min, and the crash impact created a severe air blast in the valley. The rock avalanche has also resulted in debris blocking the Raunthi gad valley. Estimated Morphological Obstruction Index (MOI) and Hydro-morphological Dam Stability Index (HDSI) indicate the debris dam to be in an unstable domain.