Flash Flood Prone Areas Research Articles

A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping

This study addresses the challenges of flash flood susceptibility mapping in Yemen’s Qaa’Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen flash flood variables were retrieved using Geographic Information System (GIS) based remote sensing, setting the stage for a novel feature selection algorithm. Then, the Memo Search Algorithm (MSA), a metaheuristic approach is proposed to efficiently reduce feature space. Through comprehensive comparisons with established algorithms such as the Artificial Bee Colony (ABC) and Gray Wolf Optimizer (GWO), MSA refined the selection, identifying 'elevation’ and 'distance to streams’ as optimal factors. Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. Comprehensive evaluation revealed the boosting ensemble with MSA outperformed traditional techniques reaching 98.75% accuracy, 0.9896 Area Under the Curve (AUC), and 98.95% the harmonic mean of the precision and recall (F1-score). Precision in identifying high-risk flash flood zones was underlined via spatial prediction, confirming the integrated framework’s ability to significantly improve forecast accuracy. The findings aid disaster management with powerful geographic mapping in data-poor regions. The proposed framework is adaptable globally for flash flood-prone areas with similar constraints. As climate change is expected to increase extreme rainfall events, communities globally will need robust data-driven methodologies for flash flood susceptibility mapping. The Key recommendations of the current study include investigating hybrid feature selection methods to better enhance predictive inputs and analyzing transferability across hydro-climatic zones.

Mapping of Multiple Hazards in the Cilongkrang Sub-Watershed, Majalengka, Indonesia

The intensive agricultural activities in the Cilongkrang Sub-Watershed are causing environmental changes that can lead to various disasters. This study aims to map the spatial distribution of multiple hazards, including flash floods, erosion, and landslides. Three hazards selected because the critical condition of Cilongkrang Sub-Watershed as an upstream area caused the area to be prone to those hazards. The flash flood hazard model was created using the Flash Flood Potential Index (FFPI) method, which uses land use, slope, vegetation cover, and soil texture. The erosion hazard model was created using the Universal Soil Loss Equation (USLE) method with parameters such as erosivity, erodibility, slope length factor, crop management index, and soil conservation index. The landslide hazard model was created using the Spatial Multi-Criteria Evaluation (SMCE) method, which uses slope, landform, land use, and soil texture. The results showed that the dominant flash flood-prone areas were in the medium class of 1.556 hectares, the erosion tended to be very low, which was 2.699,5 tons/ha/year, and The Argapura Sub-District encompasses multiple regions that are highly susceptible to landslides.

Characterization of Soil Properties Influencing Flash Flood-Related Road Damage in North-Eastern Regions of Bangladesh

Flash floods in the North-Eastern region of Bangladesh pose a severe threat to road infrastructure, causing extensive damage and disruptions. This study focuses on the comprehensive characterization of soil properties that play a pivotal role in exacerbating flash flood-related road damage. The North-Eastern region, characterized by diverse geological formations, including the Surma Basin and the Meghalaya Plateau, experiences recurrent flash floods during the monsoon season. The undulating topography and a network of rivers contribute to the complexity of the hydrogeological system, influencing soil properties and their impact on road infrastructure.
 Field surveys were conducted to identify flash flood-prone areas, focusing on elevation, slope, and proximity to water bodies. Soil samples were collected at various depths and locations within vulnerable road sections, considering different land uses and topographical features. Laboratory analyses included particle size distribution, moisture content determination, soil classification, and cohesion tests. The results of this study highlight specific road sections vulnerable to flash floods, providing a foundation for targeted soil characterization. Key soil properties influencing road damage include high clay content, poor drainage characteristics, and low cohesion. The results also revealed the correlation between vulnerable road sections and specific combinations of soil properties, allowing for the identification of hotspots prone to flash flood-related damage.
 Finally, this study contributes to an enhanced understanding of the soil properties influencing flash flood-related road damage in the North-Eastern region. The identified key soil characteristics offer valuable insights for road infrastructure planning and design, emphasizing the need for mitigation measures such as improved drainage systems and the use of appropriate roadbed materials to enhance overall road resilience in flood-prone areas.

Identification of the key driving factors of flash flood based on different feature selection techniques coupled with random forest method

Study regionThe mountainous area of Guangdong Province, China. Study focusA random forest coupling with feature selection (RF-FS) method framework, containing random forest coupling with shadow variable search, random forest coupling with recursive feature elimination, and random forest coupling with genetic algorithm, is introduced to identify the key driving factors (KDFs) of flash flood that can be used to explain the flash flood process and generate the flash flood risk map efficiently.New hydrological insights for the region: Among the KDFs identified by the RF-FS method framework, meteorological factors (maximum 1-day precipitation, total daily precipitation exceeding 95 % threshold, and maximum 5-day consecutive precipitation) are dominant, followed by hydrological factors (topographic wetness index and stream power index). Flash flood risk modeling was then developed by using the KDFs identified by the random forest coupling with genetic algorithm which is the best in the RF-FS method framework in predicting flash flood risks. Furthermore, the modeling results show that about 45 % of the mountainous areas in Guangdong Province are high flash flood-prone areas (high and extremely high-risk areas), and most of them are located in the central, eastern, and southwestern mountainous areas of Guangdong Province. The areas with flash flood-prone occurrences are generally consistent with the areas with large precipitation, implying that these areas should be prioritized for flash flood prevention and early warnings.

An Integrated Multidimensional Resilience Index for urban areas prone to flash floods: Development and validation

Resilience analysis is critical in developing flash flood risk reduction strategies in the context of global change and sustainable development. The most common method for assessing resilience is index-based. Nevertheless, the resulting indices typically fail to represent resilience's multidimensional character since they frequently disregard all involved dimensions (i.e., social, economic, environmental, physical, institutional, and cultural). Furthermore, regional resilience indices are rarely externally validated in urban areas prone to flash flooding because the required data are limited and flash flooding does not occur concurrently throughout the study region. This research developed and validated a regional Integrated Multidimensional Resilience Index (IMRI) in urban flash flood-prone areas to address the aforementioned knowledge gaps. The Monte Carlo method enabled internal validation of the IMRI following uncertainty and sensitivity analyses. Latent Class Cluster Analysis (LCCA) was used to characterize resilience, leveraging resulting regional spatial patterns. The findings obtained revealed that the most resilient urban areas have greater social and cultural resilience, while the least resilient urban areas should strengthen their social and institutional resilience. Validation results demonstrated a low bias between the IMRI scores and the control statistics derived from the Monte Carlo analysis as well as a higher than 80 % probability of not getting variations in the resilience categories, confirming the robustness of the IMRI. Through LCCA, five distinct regional spatial patterns of resilience were identified. The methodological approach deployed here enabled the identification of the underlying characteristics that determine the urban system's resilience to flash flooding, thereby supporting the formulation of resilience-building strategies for each dimension and urban area under consideration.

Geospatial Modeling Based-Multi-Criteria Decision-Making for Flash Flood Susceptibility Zonation in an Arid Area

Identifying areas susceptible to flash flood hazards is essential to mitigating their negative impacts, particularly in arid regions. For example, in southeastern Sinai, the Egyptian government seeks to develop its coastal areas along the Gulf of Aqaba to maximize its national economy while preserving sustainable development standards. The current study aims to map and predict flash flood prone areas utilizing a spatial analytic hierarchy process (AHP) that integrates GIS capabilities, remote sensing datasets, the NASA Giovanni web tool application, and principal component analysis (PCA). Nineteen flash flood triggering parameters were initially considered for developing the susceptibility model by conducting a detailed literature review and using our experiences in the flash food studies. Next, the PCA algorithm was utilized to reduce the subjective nature of the researchers’ judgments in selecting flash flood triggering factors. By reducing the dimensionality of the data, we eliminated ten explanatory variables, and only nine relatively less correlated factors were retained, which prevented the creation of an ill-structured model. Finally, the AHP method was utilized to determine the relative weights of the nine spatial factors based on their significance in triggering flash floods. The resulting weights were as follows: rainfall (RF = 0.310), slope (S = 0.221), drainage density (DD = 0.158), geology (G = 0.107), height above nearest drainage network (HAND = 0.074), landforms (LF = 0.051), Melton ruggedness number (MRN = 0.035), plan curvature (PnC = 0.022), and stream power index (SPI = 0.022). The current research proved that AHP, among the most dependable methods for multi-criteria decision-making (MCDM), can effectively classify the degree of flash flood risk in ungauged arid areas. The study found that 59.2% of the area assessed was at very low and low risk of a flash flood, 21% was at very high and high risk, and 19.8% was at moderate risk. Using the area under the receiver operating characteristic curve (AUC ROC) as a statistical evaluation metric, the GIS-based AHP model developed demonstrated excellent predictive accuracy, achieving a score of 91.6%.

Agricultural Challenges and Adaptation for Changing Climate: A Study on Early Flash Flood-prone Areas in Bangladesh

Agricultural Challenges and Adaptation for Changing Climate: A Study on Early Flash Flood-prone Areas in Bangladesh

Embedded Feature Selection and Machine Learning Methods for Flash Flood Susceptibility-Mapping in the Mainstream Songhua River Basin, China

Mapping flash flood susceptibility is effective for mitigating the negative impacts of flash floods. However, a variety of conditioning factors have been used to generate susceptibility maps in various studies. In this study, we proposed combining logistic regression (LR) and random forest (RF) models with embedded feature selection (EFS) to filter specific feature sets for the two models and map flash flood susceptibility in the mainstream basin of the Songhua River. According to the EFS results, the optimized feature sets included 32 and 28 features for the LR and RF models, respectively, and the composition of the two optimal feature sets was similar and distinct. Overall, the relevant vegetation cover and river features exhibit relatively high effects overall for flash floods in the study area. The LR and RF models provided accurate and reliable flash flood susceptibility maps (FFSMs). The RF model (accuracy = 0.8834, area under the curve (AUC) = 0.9486) provided a better prediction capacity than the LR model (accuracy = 0.8634, AUC = 0.9277). Flash flood-prone areas are mainly distributed in the south and southwest and areas close to rivers. The results obtained in this study is useful for flash flood prevention and control projects.

Assessment of the performance of satellite rainfall products over Makkah watershed using a physically based hydrologic model

Makkah region is one of the most flash flood-prone areas of Saudi Arabia due to terrain characteristics and the synoptic-scale weather conditions that intensify through interaction with the local topography causing high convective short-lived rainfall events, although these conditions are quite infrequent. Most of these events last for less than two hours. This study aims to assess the performance of five satellite precipitation products over a 1725 km2 sparsely gauged, arid basin. A fully distributed, physically based hydrologic model was forced by the five satellite precipitation products, and the evaluation included the hydrographs and runoff maps predicted by the model. Moreover, the propagation of the satellite rainfall errors into runoff predictions was quantified. Large variations and significant biases were found in satellites precipitation estimates compared to the available ground rainfall measurements. The Early IMERG product showed the best agreement with the reported total rainfall accumulations followed by Late IMERG while the other products significantly underestimated precipitation accumulations. Comparison with estimated runoff peaks showed that the Early IMERG product has the lowest errors in runoff peaks. Therefore, the hydrographs produced by the Early IMERG product were used as a reference to quantify the propagation of satellite precipitation errors into runoff predictions over the Makkah watershed. The results clearly indicated that both systematic and random rainfall errors were significantly amplified in runoff predictions.

Stacking state-of-the-art ensemble for flash-flood potential assessment

It is clear that flash flood events are on the rise, and the detection of flash flood-prone areas has now become a top priority for governments throughout the world. Flash-flood potential mapping can provide very useful information that can be used in order to adopt the most appropriate measures to reduce the risk of these phenomena. A precise model is needed in order to predict flash flood potential, which can help to improve flood management strategies, as it can be used to develop more effective flood management methods. In this present research, Index of Entropy (IOE), Logistic Regression (LR), four machine learning models (, Support Vector Machine (SVM), Classification and Regression Tree (CART), Naive Bayes (NB), k-Nearest Neighbour (k-NN)) and their 5 stacking ensembles were used to construct a new GIS-based framework for flash flood-potential mapping in the Suha River Basin, Romania. For the purposes of running the models, 8 topographical and environmental factors (slope, land use, plan curvature, profile curvature, convergence index, aspect, hydrological soil groups and tpi), along with 111 torrential locations were used to run the five applied models. In order to facilitate training and validation purposes, the entire dataset was divided into 70:30 parts. ROC Curve and statistical measures were employed in order to verify the model reliability and also validate the results. It is noteworthy here that in the training procedure, the stacking ensemble constructed through the combination of all machine learning and statistical learning models (LCkNS) was better than all the other models with a maximum accuracy of 0.972. The models used, revealed that between 10% and 22% of Suha river basin is occupied by high and very high flash-flood potential. As a result, the data generated by this research provides reliable results that can assist policymakers at the local and national levels in developing a concrete strategic plan to reduce flood frequency in their area by utilizing a flood warning system.

Spatiotemporal dependence of soil moisture and precipitation over India

Anthropogenic climate change has impacted almost all phases of the global water cycle. Growing consensus asserts that extreme precipitation events will only rise in the years to come. However, an increase in extreme precipitation events does not necessarily correspond to higher flood risk. Much onus lies on the antecedent conditions before the storm events. Despite the importance of Soil Moisture (SM) – Precipitation (P) dependence in runoff generation, relatively few studies have unraveled the SM – P dependence. Previous studies were constrained by the direct trivial relationship existing between SM and P, and hence there is a need to understand direction and dynamical interdependency. We employed Event Coincidence Analysis (ECA) to identify and quantify the preconditioning of P extremes by soil moisture (SM) anomalies. High precursor coincidence rate (greater than45%) was obtained for traditional flash flood-prone areas over India - Ganga river basin, West-flowing rivers of Kutch and Saurashtra including Luni, inland drainage of Rajasthan and Narmada river basin, indicating the robustness of the approach. The trigger coincidence rate reveals strong SM-P coupling over central India. Our results indicate the applicability of ECA in characterizing the spatiotemporal patterns of SM-P dependence over India.

Flash Flood Risk Assessment and Driving Factors: A Case Study of the Yantanxi River Basin, Southeastern China

In the context of climate change, the impact of extreme precipitation and its chain effects has intensified in the southeastern coastal region of China, posing a serious threat to the socioeconomic development in the region. This study took tropical cyclones–extreme precipitation–flash floods as an example to carry out a risk assessment of flash floods under climate change in the Yantanxi River Basin, southeastern China. To obtain the flash flood inundation characteristics through hydrologic–hydrodynamic modeling, the study combined representative concentration pathway (RCP) and shared socioeconomic pathway (SSP) scenarios to examine the change of flash flood risk and used the geographical detector to explore the driving factors behind the change. The results show that flash flood risk in the Yantanxi River Basin will significantly increase, and that socioeconomic factors and precipitation are the main driving forces. Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the risk of flash floods is expected to increase by 88.79% and 95.57%, respectively. The main drivers in the case study area are GDP density (q = 0.85), process rainfall (q = 0.74), asset density (q = 0.68), and population density (q = 0.67). The study highlights the influence of socioeconomic factors on the change of flash flood disaster risk in small river basins. Our findings also provide a reference for regional planning and construction of flood control facilities in flash flood-prone areas, which may help to reduce the risk of flash floods.

Hybrid-based Bayesian algorithm and hydrologic indices for flash flood vulnerability assessment in coastal regions: machine learning, risk prediction, and environmental impact

Natural hazards and severe weather events are a matter of serious threat to humans, economic activities, and the environment. Flash floods are one of the extremely devastating natural events around the world. Consequently, the prediction and precise assessment of flash flood-prone areas are mandatory for any flood mitigation strategy. In this study, a new hybrid approach of machine learning (ML) algorithm and hydrologic indices opted to detect impacted and highly vulnerable areas. The obtained models were trained and validated using a total of 189 locations from Wadi Ghoweiba and surrounding area (case study). Various controlling factors including varied datasets such as stream transport index (STI), stream power index (SPI), lithological units, topographic wetness index (TWI), slope angle, stream density (SD), curvature, and slope aspect (SA) were utilized via hyper-parameter optimization setting to enhance the performance of the proposed model prediction. The hybrid machine learning (HML) model, developed by combining naïve Bayes (NïB) approach and hydrologic indices, was successfully implemented and utilized to investigate flash flood risk, sediment accumulation, and erosion predictions in the studied site. The synthesized new hybrid model demonstrated a model accuracy of 90.8% compared to 87.7% of NïB model, confirming the superior performance of the obtained model. Furthermore, the proposed model can be successfully employed in large-scale prediction applications.

Established Rice Ratoon for Improving Cropping Intensity and Income

Transplanted aman rice suffers from frequent flash flood damage in northern Bangladesh and thus grain yield decreases. An experiment was conducted at the RDRS Farm, Rangpur, Bangladesh to evaluate production opportunity and income generation from growing ratoon rice in between boro and aman seasons during May to July in fallow land as additional harvests. Ratoon seedlings of ACI-2 boro hybrid rice was transplanted and tested against the performance of conventional transplanted BR11 and BRRI dhan46. BR11 gave higher grain yield and profit than ratoon cropping. However, ratoon crop required only 75-80 days for maturity. Thus, it would be possible to incorporate rice ratoon cropping in fallow lands immediately after harvest of boro rice in April- May as additional crop. After harvests of the ratoon rice, farmers can grow traditional aman varieties as conventional practice. If ratoon of ACI-2 (hereafter ratoon rice)-BRRI dhan46 cropping system is practiced then total grain yield from the system was 8.42 t ha-1, which was almost double than conventional BR11 or BRRI dhan46 cultivation. Total net income of boro and ratoon rice-BRRI dhan46 system was also higher (Tk 55,090 ha-1) compared to traditional BR11 aman rice cultivation (Tk 35,802 ha-1). The overall result indicated that just immediate after boro rice harvests, growing rice from ratoon seedlings of ACI-2 followed by BRRI dhan46 in aman season provided higher net profit than traditional cultivation of BR11. The result also implies that growing rice by using ratoon seedlings can ensure three harvests (boro rice-ratoon rice-aman rice) in the same year instead of growing two traditional rice (boro rice-aman rice) crops in a year with additional net profit of Tk 19,288 ha-1 year-1. Moreover, use of rice ratoon seedlings might help farmers in intensification of crop in flash flood prone areas of the country. Ann. Bangladesh Agric. (2021) 25 (1) : 33-41

Flood risk public perception in flash flood-prone areas of Punjab, Pakistan.

Floods due to higher severity of destruction are considered the most destructive natural hazards in the world. Initiating the appropriate strategies of disaster risk reduction is necessary to understand risk perception. This study attempted to examine the flood risk public perception in flash flood-prone areas of Punjab, Pakistan. A simple random sampling technique was used for collecting the data of 560 household respondents, and a logistic regression model was employed to make out the factors of flood risk perception. In classification of low and high perceived risk of relevant indicators, flood risk perception index was constructed. Risk perception is significantly influenced by socioeconomic factors which have a direct impact on disaster preparedness and potential adaptive capacities. After that, potential correlation of risk perception with the demographic status of respondents was investigated in this study. Empirical estimates indicated as respondents' schooling, ownership of house, size of household, employment status and past flood experience significantly influence flood risk perception. Risk perception determinants also diverse among both communities portray spatial differences. Inadequate protection measures from public authorities and institutions, limited preparedness regarding actions of private mitigation, reduced intensity of reliance in institutions and authorities are major reasons for high risk and lower mitigation in these flash flood-prone areas. The outcomes of this research can facilitate to understand flood risk perception and its factors for conniving appropriate management plan of flood risk and communication strategies. Furthermore, this research can help consider multidimensional flood risks and its spatial vibrancy from the perspective of social science.

Type of terrain and infant and young child feeding practices: cross-sectional study findings on children below 2 years of age from northern Bangladesh

IntroductionOptimal feeding practices are vital for the subsistence, adequate nutrition, physical growth and mental development of infants and young children. This study aimed to examine the prevalence of core infant...

Developing an integrated multiscale rainfall-runoff and inundation model: Application to an extreme rainfall event in Marikina-Pasig River Basin, Philippines

Study regionMarikina–Pasig basin, Philippines Study focusA multiscale rainfall-runoff and inundation model was developed to simulate the severe flooding caused by a high-intensity short-duration storm rainfall event in the urban domain. The model used the 2D shallow-water flow and finite volume schemes with second-order space–time accuracy to automatically incorporate the runoff into the flood inundation. The adaptive mesh refinement scheme—a variable grid-sizing technique—was implemented to dynamically refine the mesh from a watershed-scale to a more precise inundation in a subcatchment floodplain. The digital terrain and surface models were used to represent the variations in flood propagation along buildings and artificial barriers in the urban area. Additionally, the simulated flood depths and extent were validated by comparison with the observed data. New hydrological insightThe severe inundation produced by the extreme rainfall event was perceived to be caused by a combination of fluvial and pluvial flooding; exacerbated by the domain’s flat and low-lying elevation and its geographical features acting as a flood water receiving catchment accrued from the adjacent mountainous regions. The proposed method can be used to identify the flood depth and water accumulation, estimated peak time, potential flash-flood prone areas, and imperceptible flood origins without the need to establish first the water level and stream discharge boundary conditions. The domain-specific flood simulations can be used for early warning and to identify a broader scope of possible evacuation points during storm-based flooding events.

Risk Perception, Risk Communication, and Mitigation Actions of Flash Floods: Results from a Survey in Three Types of Communities

In order to improve the decision-making of risk management and enhance community resilience to flash floods, the perception of risks, communication of warnings, and mitigation actions concerning flash floods were investigated in this study. The survey involves 280 participants from three types of communities in flash flood-prone areas. Results show that: (i) About 55.4% of community participants misperceived or underestimated the risk of flash floods, especially in the suburban communities, and people had misconceptions about the safety of crossing fast-flowing water, even though most of them had experienced flash flood hazards. (ii) In total, 67.9% of participants indicated that they had at some point received a flash flood warning. The perception of accuracy was related to trust in flash flood warnings, but they were different constructs for some individuals. Moreover, residents in the rural community and suburban community reported a closer social communication with neighbors, which would greatly influence inhabitants’ attitudes and behaviors towards the flash flood warnings and mitigation actions. (iii) Most of the participants indicated they would take some protective action when they received a warning. Risk perceptions and risk communications influence the mitigation actions in the community. Significant variables in the rural community and non-rural community were explored, and some important suggestions are highlighted. These findings suggest that risk perception and risk communication in neighborhoods help people to decide what action to take in the given scenarios, contribute to enhancing the community resilience, and contribute to coping with future flash floods in a more specific and effective way.

Prioritization of Flash Flood-Prone Areas in Small Coastal Basins around the Mediterranean Using Geomorphological Variables

The present study aims to model flash flood risk in small coastal watersheds in areas that are characterized by Mediterranean climate through extensive morphometric analysis which can prove invaluable for the investigation of flood risk, in ungauged watersheds, where flash floods are frequent. The available topographic data (EU-DEM) are analyzed through Geographic Information Systems (GIS) to produce all the secondary variables that are necessary for this morphometric analysis. Watershed prioritization techniques that are applied on geomorphological variables have proven to be an effective way of estimating the relative flash flood risk in a sub-watershed level. A series of morphometric parameters are used (bifurcation ratio, drainage frequency, drainage density, drainage texture, length of overland flow, circularity ratio, form factor, elongation ratio) which have an effect on flood risk. In small watersheds, with intermittent runoff, this effect can be different than in larger watersheds, so our methodology differs significantly from the methodology other researchers use. The compound factor is calculated by aggregating the assigned ranks of these morphometric indices and the sub-watersheds are prioritized according to their flash flood risk. The study area is located in the island of Samos, in Eastern Greece, where flood events are usual and pose a risk to villages and infrastructure around the island. The selected watershed (Imvrasos river) is divided into several sub-watersheds (W-1 to W-8) and a series of morphometric indices are calculated and evaluated through statistical procedures and by applying prioritization techniques, in order to locate the sub-basins that have the highest risk to flash floods. Sub-watersheds W-2 and W-3 (on the southern part of Imvrasos area) show the highest prioritization values, and should be prioritized for better watershed management planning.

Factoring Multi-Hazard Risk Perception in Risk Assessment and Reduction Measures in Landslide and Flash Flood Prone Areas – A Case Study of Sichon District, Nakhon Si Thammarat Province, Thailand

This study’s purpose is to analyze the degree of risk and vulnerability involved in landslide and flash flood prone community areas in Thepparat sub-district, Sichon district, Nakhon Si Thammarat province, Thailand. It also aims to analyze and understand the socio-economic impacts on the community at the household level, and assess the community’s risk and vulnerability by examining its risk perception. The risk perception was done using focus group discussions and a questionnaire survey with key stakeholders. It mainly focused on how the risk of landslides and flash floods influences the community’s risk perceptions, which was tested in two parts: at the organizational and community levels by focusing on government officials and households, respectively. A correlation matrix was used to understand the relationship of the indicators selected. The Pearson correlation result has shown that the degree of risk awareness positively correlates with the income level, education level, and controllability, signifying that the risk of landslides and flash floods influences household risk perceptions. The qualitative assessment recommends community-level preparedness as being paramount to reduce the risk for a resilient community.