Inundation Depth Research Articles

Assessment of socio-economic strategies for managing regional flood risk in an urban coastal catchment

With the cascading flooding events over the past decades, several structural and non-structural measures have been effectively implemented for improved flood management. However, the uneven impacts of the flooding events have highlighted that socio-economic inequality amongst the community needs to be addressed along with hazards to devise long term and sustainable flood management strategies. This study aims to evaluate the impact of socio-economic adaptive scenarios over an urban coastal catchment for reduction in flood risk. Flood risk is estimated utilizing socio-economic aspects (vulnerability) and flood inundation (hazard), for the highly flood-prone coastal city of Mumbai, India. A multivariate approach is used for assessing socio-economic vulnerability, starting with Principal Component Analysis followed by Data Envelopment Analysis, for various socio-economic scenarios, i.e., by reducing percentage of certain feasible socio-economic sensitivity indicators, to identify the key indicators, which contribute to significant change in vulnerability. The flood hazard is assessed through a comprehensive hydrodynamic flood modelling approach to quantify the flood inundation depth and extent. The flood risk is, thereafter, evaluated by combining socio-economic vulnerability and flood hazard using bivariate risk mapping approach. The results indicate that, with the decrease in alteration percentage of certain feasible socio-economic sensitivity indicators, the overall vulnerability and flood risk may be reduced. The areas under very high and high vulnerability classes reduce gradually for all scenarios as the percentage reduction for various indicators is varied from 5 % to 15 %, total illiteracy being the most significant indicator. This study marks the first effort to discern socio-economic vulnerability indicators that have the potential to significantly diminish flood risk, thereby constituting a viable long-term flood risk mitigation strategy. This novel framework is generic and applicable to any flood prone catchments, to devise appropriate long-term flood management measures.

Pengembangan Potensi Wisata dan Kesesuaian Lahan Wisata terhadap Resiko Tsunami di Kawasan Teluk Pacitan

The southern coastal region of Pacitan Regency, particularly in six districts, namely Ngadirejo, Sudimoro, Kebonagung, Donorojo, Pringkuku, and Pacitan District, is classified as a high-risk tsunami red zone. Based on the research findings, the Pacitan Bay area consists of two villages, namely Sidoarjo Village and Ploso Village in Pacitan Distict. Sidoarjo Village covers an area of 8.36 km², while Ploso Village covers 3.66 km², making the total area 12.02 km² or 1202 hectares. The analysis of land suitability for tourism in relation to tsunami risk aims to assess the level of compatibility of a tourist area located in a tsunami-prone region. The environmental vulnerability parameters include elevation, land slope, coastal morphology, land use, distance from the coastline, and distance from rivers. Additionally, bathymetric data and tsunami run-up based on historical tsunami events in the southern coastal areas of Java Island are utilized to assess the extent and depth of tsunami inundation. In the analysis of tourism suitability based on tsunami disaster mitigation, the suitable tourism area is located in the eastern part of Sidoarjo Village and the entire Ploso Village. These areas fall into the high tsunami risk zone, but their tourism potential is also high due to the coastal location of tourist attractions

Assessing Watershed Flood Resilience Based on a Grid-Scale System Performance Curve That Considers Double Thresholds

Enhancing flood resilience has become crucial for watershed flood prevention. However, current methods for quantifying resilience often exhibit coarse spatiotemporal granularity, leading to insufficient precision in watershed resilience assessments and hindering the accurate implementation of resilience enhancement measures. This study proposes a watershed flood resilience assessment method based on a system performance curve that considers thresholds of inundation depth and duration. A nested one- and two-dimensional coupled hydrodynamic model, spanning two spatial scales, was utilized to simulate flood processes in plain river network areas with detailed and complex hydraulic connections. The proposed framework was applied to the Hangjiahu area (Taihu Basin, China). The results indicated that the overall trend of resilience curves across different underlying surfaces initially decreased and then increase, with a significant decline observed within 20–50 h. The resilience of paddy fields and forests was the highest, while that of drylands and grasslands was the lowest, but the former had less recovery ability than the latter. The resilience of urban systems sharply declined within the first 40 h and showed no signs of recovery, with the curve remaining at a low level. In some regions, the flood tolerance depth and duration for all land use types exceeded the upper threshold. The resilience of the western part of the Hangjiahu area was higher than that of other regions, whereas the resilience of the southern region was lower compared to the northern region. The terrain and tolerance thresholds of inundation depth were the main factors affecting watershed flood resilience. The findings of this study provide a basis for a deeper understanding of the spatiotemporal evolution patterns of flood resilience and for precisely guiding the implementation and management of flood resilience enhancement projects in the watershed.

Characteristics of Deformation and Damage and Acoustic Properties of Sandstone in Circular Tunnel Morphology under Varying Inundation Depths

When water damage occurs in a mine, variations in the immersion levels of tunnels at different burial depths can be observed. There is a significant relationship between the stability of the surrounding rock and the depth of immersion. Therefore, studying the deformation and damage characteristics of sandstone with circular holes at varying immersion depths, along with their acoustic properties, plays a crucial role in maintaining the stability of water-rich roadways. The TAW-2000 press and static strain system were utilized to investigate the mechanical properties, crack evolution, and deformation field distribution of sandstone with circular holes at varying immersion depths. Additionally, this study analyzed the impact of immersion depth on the characteristic parameters of acoustic emission. The results indicate that immersion depth is negatively correlated with the compressive strength and modulus of elasticity of sandstone; as immersion depth increases, the duration of the compression and yield phases of the rock samples also increases, while the duration of the elastic phase remains relatively unaffected. Furthermore, greater immersion depths correspond to a decrease in the total number of cracks, although the proportion of tensile cracks increases, making the formation of secondary cracks less likely. The frequency of acoustic emission events (transient elastic waves generated by the formation, extension, or closure of tiny cracks within the rock) shows a closely correlated dynamic with the stress–time curve of the rock sample. The acoustic emission ringing counts generated by rock samples under submerged water conditions tend to stabilize with a slight increase before signs of rupture appear. Additionally, the cumulative total energy of acoustic emissions from the rock samples decreases as the water level rises. These research findings provide significant reference value for addressing issues related to water immersion and the extent of water saturation in roadways within rock engineering.

A Convolutional Neural Network-Weighted Cellular Automaton Model for the Fast Prediction of Urban Pluvial Flooding Processes

Abstract Deep learning models demonstrate impressive performance in rapidly predicting urban floods, but there are still limitations in enhancing physical connectivity and interpretability. This study proposed an innovative modeling approach that integrates convolutional neural networks with weighted cellular automaton (CNN-WCA) to achieve the precise and rapid prediction of urban pluvial flooding processes and enhance the physical connectivity and reliability of modeling results. The study began by generating a rainfall-inundation dataset using WCA and LISFLOOD-FP, and the CNN-WCA model was trained using outputs from LISFLOOD-FP and WCA. Subsequently, the pre-trained model was applied to simulate the flood caused by the 20 July 2021 rainstorm in Zhengzhou City. The predicted inundation spatial distribution and depth by CNN-WCA closely aligned with those of LISFLOOD-FP, with the mean absolute error concentrated within 5 mm, and the prediction time of CNN-WCA was only 0.8% that of LISFLOOD-FP. The CNN-WCA model displays a strong capacity for accurately predicting changes in inundation depths within the study area and at susceptible points for urban flooding, with the Nash-Sutcliffe efficiency values of most flood-prone points exceeding 0.97. Furthermore, the physical connectivity of the inundation distribution predicted by CNN-WCA is better than that of the distribution obtained with a CNN. The CNN-WCA model with additional physical constraints exhibits a reduction of around 34% in instances of physical discontinuity compared to CNN. Our results prove that the CNN model with multiple physical constraints has significant potential to rapidly and accurately simulate urban flooding processes and improve the reliability of prediction.

Flood simulation and impact analysis of Xun River under backwater condition

Abstract The intersection of river channels is easy to cause the backside of the main flow, which greatly influences the change of hydrological conditions of river channels. To study the changing characteristics of river flood under the condition of uplift, a one-dimensional hydrodynamic model was established to simulate the evolution process of river flood under the condition of seeking river uplift. Factors such as distance, height, flood speed, inundation area, inundation depth, and flood speed were selected to analyze the changing rules of the influence of river bullying. The results show that the influence degree of the river following is closely related to the size of the flood. Under the same earthquake magnitude, the backwater distance, backwater height, inundation area, inundation depth, and inundation speed of sub-river flood increase with the increase of flood magnitude, while the flood speed decreases. When the magnitude of the flood is once in 200 years, the flood elements reach the maximum, the reflux distance is 8, 368 m, the reflux height is 3.2 m, and the velocity is reduced by 43.2%. This study can provide relevant data for the flood control work in the Sun River basin and provide a reference for the scheme design of tributary interchanges.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

Mitigating severe losses caused by pluvial floods in urban areas with dense population and property requires effective flood prediction for emergency measures. Physics-based models face issues with low computational efficiency for real‐time flood prediction. An alternative approach for rapid prediction instead of physics-based models is to predict from a data-driven perspective. However, data-driven approaches for urban flood prediction are often perceived as “black box” and might raise concerns. In this study, we propose an explainable deep learning (DL) approach for rapid urban pluvial flood prediction with enhanced transparency using a convolutional neural network (CNN) and the explainable artificial intelligence (AI) framework Shapley additive explanation (SHAP). We process a systematic stepwise feature selection process and establish a CNN model considering topography, drainage networks and rainfall to predict maximum inundation depths. Then, SHAP is applied to provide trustworthy explanations for the decision making in model results. The results show that: 1) Forward selection can offer insights into selecting effective input variables for improved predictions and promote understanding of DL modelling. The spatial pattern of inundation depths predicted by the proposed CNN model shows good agreement with those predicted by the physics-based model, demonstrated by average correlation coefficient (CC) and mean absolute error (MAE) values of 0.982 and 0.021 m, respectively. 2) The CNN model substantially outperforms the physics-based model in computational speed when using the same hardware, achieving speedups of 210 times on GPU and 51 times on CPU in the case study (575167 grid cells, 14.38 km2). Particularly, it can still achieve good performance on a CPU-only standard laptop without high-performance hardware, with only a modest increase in computational time. 3) The SHAP explainable analysis confirms that the CNN model correctly captures the relationships between input variables and water depth, revealing a reasonable decision-making process, enhancing its transparency. The explainable DL approach incorporating SHAP for rapid urban pluvial flood prediction is promising to build trust among stakeholders and provide a trustworthy reference for prompt measures aiming at saving lives and protecting assets during flood emergencies. Additionally, the proposed DL approach can potentially be further expanded to analyze the causes of urban flooding events and serve as a foundation for exploring the transferability of data-driven urban flood prediction, providing benefits for better urban flood risk management.

Projecting urban flood risk through hydrodynamic modeling under shared socioeconomic pathways

Under future climate change, accurate risk assessment of urban flooding disasters is paramount for effective adaptation and mitigation strategies. However, conventional indicator-based assessment methods often fall short of accurately capturing the complexity of flooding dynamics. Current research predominantly focuses on predicting future hazard shifts while overlooking changes in other critical indicators. In this study, we establish a comprehensive index system for risk assessment, and quantified future changes in most indicators, utilizing the InfoWorks ICM model for hazard simulation and the CLUMondo model for land use predictions. Based on risk assessment results and regional characteristics, we further analyze the key factors driving future risk and discuss corresponding measures. The results indicate an exacerbation of future urban flood risk, with an 18% increase in high risk areas, primarily concentrated in the center of the study area. The dominant indicators are inundation depth and land use over the whole study area. However microtopography significantly affects risk in low-lying areas. Overall, under higher emission scenarios, the influence of GDP and population rises. These findings offer methodological insights for future urban flood risk assessment research and provide policymakers with valuable guidance to develop targeted adaptation measures in response to climate change.

A novel urban flood risk assessment framework based on refined numerical simulation technology

Due to urbanization and climate change, urban areas are becoming more susceptible to flooding, posing significant risks to human life and property. Numerical simulation techniques can provide valuable support in mitigating urban flood risks. The objective of this study is to establish a novel framework for flood risk assessment based on refined urban flood simulations. To achieve refined urban flood simulations, the Torricelli’s law is incorporated into a two-dimensional fluid dynamics model, and a numerical analysis model is developed to estimate the inundation within buildings and accurately simulate the entire inundation process. The model was used to reproduce a real flood event in Omihachiman City, Japan, with the Nash-Sutcliffe Efficiency (NSE) values of 0.6488 and 0.7182, demonstrating the credibility of the model. Based on the simulation results, the hydrological causes of the flood event were analyzed, and flood prevention measures were proposed. After implementing these measures, the maximum water depths at the study area were reduced to 0.14 m, 0.62 m, and 0.12 m, respectively, proving the effectiveness of the flood prevention measures. To address the issue of inundation depth not fully reflecting flood risk, the instability of human bodies in floodwaters is conducted to evaluate flood risk for adults and children in the target area. In response to increasingly frequent extreme rainfall events, the extreme precipitation events of three different return periods (10-year, 50-year, and 100-year) are used to assess the flood risk and the effectiveness of flood prevention measures. Based on the assessment results, the flood prevention measures not only reduced the flood risk levels but also decreased the flood risk area, further confirming their effectiveness.

Towards multi-variable tsunami damage modeling for coastal roads: Insights from the application of explainable machine learning to the 2011 Great East Japan Event

The accurate assessment of tsunami-induced damage to coastal roads is crucial for effective disaster risk management. Traditional approaches, reliant on univariate fragility functions, often fail to capture the complex interplay of variables influencing road damage during tsunami events. This study addresses this limitation by employing machine learning techniques on an extensive dataset compiled after the 2011 Great East Japan tsunami. The dataset, enriched with additional explicative variables accounting for the hydraulic features of the event and the physical characteristics at roads’ location, enables a comprehensive analysis of road damage mechanisms. Results indicate that while inundation depth remains a significant predictor, factors such as wave approach angle, road orientation and potential overflow from inland watercourses also play critical roles.

Animation tsunami modelling in 3D to determine shelter and evacuation routes

ABSTRACT Indonesia is located on the Pacific Ring of Fire, making the country prone to earthquakes and tsunamis. Indonesia, especially the southern region of Java, has the potential to be hit by a megathrust earthquake triggering waves much higher than before. Determining evacuation routes and locations as a part of disaster management is essential to avoid many fatalities. This research aims to determine evacuation routes and shelters using photogrammetry-based 3D animation. This paper applied experimental research for modelling and simulation approach with the stages of generating a 3D orthomosaic map, predicting tsunami magnitude, determining the height and distribution of waves, modelling and simulating 3D animation as well as marking evacuation routes and shelter locations. 3D orthomosaics are created based on aerial photos (UAV) drones. Future tsunami data are used to estimate the extent and depth of inundation that may occur. This research uses the Teluk Penyu area in Cilacap, Indonesia, as a study site. The tsunami disaster caused by an earthquake magnitude 7.8 centred in the south of West Java hit this city in 2006. This research produces a 3D animation model that can visualise the extent and depth of tsunami inundation to determine evacuation routes and shelter buildings.

Neglected pathways of heavy metal input into agricultural soil: Water–land migration of heavy metals due to flooding events

Flooding, carrying sediments, inundates farmlands across the world due to extreme adverse weather conditions. The casualties and property damage associated with flooding are important direct impacts. However, there is currently insufficient understanding of the remobilization and distribution of heavy metals (HMs) caused by flooding. Few studies have specifically considered flooding as a pathway for HMs contamination of soil. Herein, a novel methodological framework for revealing the input pathways of HMs in agricultural soils in mining-intensive areas is proposed and applied. Flooding is considered one of the pathways for HMs inputs during source apportionment. The results demonstrated a high degree of overlap between the distribution characteristics of major HMs in agricultural soils and sediments. The degree of soil Cd pollution was significantly positively correlated with the inundation depth in the flooded area. It took 8.4–11.5 times of flood inundation or 98.5–119.9 years of accumulation of atmospheric deposition to reach HMs contamination levels in the soil of the study area. Flooding brought in most of the soil Cd, while atmospheric deposition was the primary input pathway for soil Pb and Zn. Our results identified the role of flood inundation on the input of HMs in mining-intensive areas. These results demonstrated the value of our framework for studying the impact of flooding on HMs in agricultural soils from the perspective of input pathways, providing new insights not only into identifying the sources of soil HMs but also into enhancing understanding of the impact of flooding on soil environments. With the potential increase in the frequency and intensity of flooding inundating farmlands in the future, it is essential to consider flooding as a pathway for HMs inputs in order to comprehensively assess their environmental impact.

Assessment on vulnerability of road networks considering the dynamic impact of urban waterlogging and the mitigation effect of LID measures

The urban waterlogging stemming from the rainstorm exerts a considerable adverse influence on road networks. Accurately evaluating the vulnerability of road networks during waterlogging is a crucial measure to alleviate flood risk and damage. Nevertheless, previous research merely considered the maximum inundation depth of waterlogging, but neglecting the dynamic variation of waterlogging. In this study, we firstly develop a coupled hydrodynamic model (IFMS-Urban + SWMM) and integrated it with a road traffic model to analyze the impact of waterlogging on road networks; then, a time factor is involved to assess the dynamic variation of flood vulnerability by using a novel index road vulnerability recovery index (RVRI); finally, the mitigatory effect of low impact development infrastructure/practices (LIDs) on the vulnerability of transportation roads under waterlogging was investigated. The results show that, both the coupled hydrodynamic model and road traffic model were verified for logical soundness and show strong adaptability. The urban waterlogging becomes increasingly severe with the increase of return period of rainstorms and leads to a corresponding rise in the vulnerability of the road network. The evaluation system of road network vulnerability becomes more comprehensive and reasonable with the consideration of the time factor. The well-arranged LIDs can effectively reduce the vulnerability of road network. This study serves as a valuable reference for dynamically assessing the vulnerability of road networks to urban waterlogging, offering guidance for urban waterlogging prevention, road traffic management, and road operation and maintenance in urbanized areas.

Nationwide Distribution and Characteristics of Low-rise Residential Areas at Risk of Flooding with Inundation Depths of 3m or Greater

Nationwide Distribution and Characteristics of Low-rise Residential Areas at Risk of Flooding with Inundation Depths of 3m or Greater

Analysis of Inundation Flow Characteristics and Risk Assessment in a Subway Model Using Flow Simulations

Subway station platforms are vulnerable to flood damage. Thus, an investigation of inundation in subway platforms is required to ensure the safety of citizens against flooding. This study analyzed and validated the inundation characteristics and safety areas in a subway station model using experimental inundation depth measurements and numerical simulations. Then by using the simulation, the effects of increased inflow to water velocity and depth were analyzed, and its impact on human models was found by using risk assessments which included specific force (M0), Flood Hazard Degree (FD), Flood Intensity Factors (FIF), toppling velocity, and sliding velocity. The flood risk assessment analysis results show that assessments using M0 could increase uncertainty by broadening the evaluation of risky areas compared to other indices. Also, the drag force applied to the human models was calculated using the simulations, which provided inundation risk values to people in subway stations. Overall, the risk assessments would provide a criterion for flood situations in subway stations.

Enhancing flood risk assessment in urban areas by integrating hydrodynamic models and machine learning techniques

Urban flood risks have intensified due to climate change and dense infrastructural development, necessitating innovative assessment approaches. This study aimed to integrate advanced hydrodynamic models with machine learning (ML) techniques to improve urban flood prediction and hazard analysis. Integrating 1D and 2D hydrodynamic models calibrated with precise parameters demonstrated exceptional predictive accuracy for flood dynamics. The high-resolution Lidar images and sophisticated modeling were used to simulate flood scenarios for the River Thames in West London. The socioeconomic data were incorporated to map vulnerable zones accurately. The findings revealed that 2D hydrodynamic models, while computationally intensive, offered superior accuracy in predicting flood dynamics. In contrast, 1D models could have been more accurate in predicting inundation depth and extent in adjacent urban areas. Advanced ML models, such as the Extra Trees-Principal Component Analysis (ET-PCA) model, further instilled confidence in the reliability of the research findings, demonstrating near-perfect predictive reliability with an R2 of 0.999. This model provided near-perfect predictions and enabled precise flood risk zoning. The integration of socioeconomic data further highlighted the vulnerability of certain urban areas. It emphasized the importance of targeted flood mitigation strategies. Beyond the specific case study, this research demonstrated the potential of combining hydrodynamic simulations with ML to enhance urban flood resilience globally. This framework helps urban planners and policymakers devise effective flood mitigation strategies and improve infrastructure resilience against future flood events.

Modeling and application of urban waterlogging resilience assessment based on PSR–NES framework and analysis of driving mechanisms

ABSTRACT Extreme weather events are escalating globally, intensifying urban waterlogging disasters and endangering inhabitants and assets while hindering socio-economic progress. This study explores factors hindering cities' waterlogging disaster mitigation using urban resilience theory. It introduces a pressure–state–response (PSR)–natural–economic–social (NES) assessment model to evaluate urban waterlogging resilience, focusing on Nanjing as a key city in flood control. Flood scenarios are simulated using the MIKE model for different rainstorm recurrence periods. Urban flooding resilience is assessed with a hybrid approach. Geodetector analysis identifies primary resilience drivers. Results validate the PSR–NES composite resilience index, showing complex resilience patterns across the study area. High-resilience areas are mainly in the east and southeast, while low-resilience areas cluster along roads and low-lying communities. Key resilience indicators include waterlogging duration, inundation depth, warning capacity, economic density, and road accessibility. These factors interact with nonlinear enhancements and synergies. The study's insights can identify urban management or flood defense infrastructure deficiencies, guiding tailored flood defense strategies for stakeholders.

Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai

Abstract. Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of a driver's individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the same frequency amplification (SFA) method to transform the design event values in hourly time series so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver, and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, that the peak rainfall occurs 2 h before the peak storm surge would cause the deepest average cumulative inundation depth. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagates upstream in the Huangpu River, resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.

Effects of building configuration and upstream buildings on pedestrian risk around ideal buildings in a floodwater–wind joint environment

Urban building affects both floodwater inundation and air flow in high-density cities, influencing the pedestrian risk within urban blocks. Previous studies have identified physical characteristics of buildings as important factors influencing either floodwater spread or the wind environment. However, the correlation between the building configuration and pedestrian risk under the combined effect of floodwater and wind is not yet fully understood. This study estimates the integrated effects of building configuration and an upstream building on the pedestrian risk around ideal buildings in a joint floodwater–wind environment. Computational fluid dynamics (CFD) simulations are performed to model the inundation depth, floodwater velocity, and wind speed at pedestrian level for single- and double-building models. The pedestrian risk in terms of the toppling instability is evaluated, and a series of laboratory flume experiments on the stability/instability of a dummy representing pedestrians in a quasi-natural state are conducted to validate the CFD simulation results. The findings show that the building configuration affects the pedestrian risk to some degree; however, the building height yields a minor effect and converging and diverging flows lead to different pedestrian risk distributions within the area of interest. Floodwater always plays a dominant role over wind in triggering pedestrian toppling, and pedestrian evacuation speeds vary depending on the circumstances. When an upstream building is added, its distance from the target building, length, and the reduced ratio relative to the reference case, as well as the interspace between its segments, influence the pedestrian risk; however, the height of the upstream building has the smallest effect on the pedestrian risk response. The study outcome can provide insights for administrators and stakeholders when implementing tailored measures against direct impacts of surging floodwater and strong wind in the context of resilient city management.

Effect of Inundation Depth, Dosage of Sago Bagasse and Husk Charcoal on Sago Palm Seedling (Metroxylon sagu) Grown in Polybags

Effect of Inundation Depth, Dosage of Sago Bagasse and Husk Charcoal on Sago Palm Seedling (Metroxylon sagu) Grown in Polybags