Flood Research Articles

An agent-based model for the assessment of the ripple effect of disaster economic losses considering firms’ adaptive behaviors

Assessing the spatial and temporal ripple effect of disaster economic loss is essential for integrated disaster risk management considering disaster countermeasures dynamically and systematically. Traditional loss estimation models based on general equilibrium theory hardly take the ripple process into account, as they just focus on the equilibrium status of economic system. However, the debate is that system will not reach equilibrium at one stroke and firms' adaptive behaviors will also change the disaster ripple losses. This paper proposes an agent-based model to investigate this phenomenon: the economic linkages among micro-agents are established based on regional input–output table and firms' production behaviors are defined according to nested production functions. The 2020 flood disaster in Hubei Province, China, is taken as an application case which demonstrated the feasibility of agent-based model for assessing the economic loss ripple effect considering firms' adaptive behaviors. Several interesting findings from the application are also drawn. (1) The speed of loss ripple is influenced by the distance to the disaster-stricken region, while the intensity of loss ripple depends on the strength of interregional industry linkages. (2) Round by round ripple effects could be found in non-disaster regions due to the lagged impacts caused by damage of production capacities in other non-disaster regions. (3) Firms' adaptive behavior reduced the ripple losses nearly by 60 % and saved CNY 5.72 billion in this case study. Notably, the gains from overproduction behavior in non-disaster regions are critical, with reducing the loss of CNY 5.8 for every CNY 1 of overproduction.

Community Participation in Reducing Flood Disaster Risk in Boyantongo Village, South Parigi Sub-district, Parigi Moutong District

Boyantongo Village is one of the villages in the South Parigi Sub-district, Parigi Moutong Regency, which has experienced flooding several times during the rainy season. Flash floods that hit Boyantongo Village occurred on August 25, 2012, causing 142 residents of Boyantongo Village to flee Flash floods in Boyantongo Village occurred again on July 11-13, 2020 flash floods occurred at 21.00 WITA causing 18 Boyantongo Village residents to flee. The purpose of this study was to determine the level of community participation in reducing the risk of flood disasters in Boyantongo Village. The study used a sample survey method, survey research is one research approach that is generally used for extensive data collection. At the same time, the data collection techniques used in this study used instruments in the form of questionnaires. The research was conducted by distributing questionnaires to 67 households. The results showed that the potential for flooding is in Hamlet I, Hamlet III, and Hamlet IV. Aspects of community understanding of basic disaster knowledge, from Hamlet I is categorized as quite good, Hamlet III is categorized as good and Hamlet is categorized as quite good. Community knowledge in disaster mitigation during floods is categorized as low. It shows that the community's knowledge of disasters is not good, as seen from the parameters of knowledge and attitudes towards disaster risk. This is because the community has not participated in socialization and training on community participation in reducing the risk of flooding in the village.

Perspectives on risk analysis and control for real-time operation of flood control systems

Flood control operation, a non-engineering measure, can efficiently manage flood disasters within a river basin. However, numerous uncertainties exit in the real-time operation of flood control systems, creating risks in decision-making. As an efficient tool to mitigate these risks, risk management has garnered increasing attention in real-time flood control operation. This communication offers a series of suggestions for future research concerning risk management in real-time flood control operation, including risk assessment, risk diagnosis, and risk control methods.

Global insights from local actions: a case study on enhancing flood disaster management efficiency in China's Greater Bay Area.

This study examines the flood disaster management network within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2015 to 2021, identifying government department involvement and influence shifts. Key findings indicate a decrease in the centrality of the Public Security Office and Department of Transportation, suggesting a strategic shift toward more specialized, technology-driven disaster management. Conversely, the Science Bureau's increased engagement, from 8.43% to 12.84%, highlights a policy shift toward scientific research and technological innovation in managing flood risks. The analysis reveals underutilized communication between the Central Committee, the Poverty Alleviation Office, and the Publicity Department, highlighting opportunities for improved integration in disaster management and public communication strategies. To address these issues, the study suggests strengthening inter-departmental collaboration to leverage technological advancements in disaster management. It also recommends integrating flood disaster management with poverty alleviation initiatives to support affected populations comprehensively. Increasing the involvement of the Publicity Department is crucial for improving timely and transparent communication of flood-related data to the public. The conclusions advocate for an adaptive, strategically planned network approach to flood disaster management in the GBA, aiming to bolster responsiveness and preparedness for future flood events.

Flood Susceptibility Mapping for Kedah State, Malaysia: Geographics Information System-Based Machine Learning Approach

ABSTRACT Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. Aim: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility map for the study area. Methods: Ten flood-influencing factors from a geospatial database were taken into account when mapping the flood-prone areas. Every element demonstrated a robust relationship with the probability of flooding. Results: The highest contributing elements for the flood disaster in the study region were drainage density, distance, and the curvature. Flood susceptibility models’ performance was validated using standard statistical measures and AUC. The ROC curves demonstrated that all ensemble models had good performance on the validation data sets (AUC = >0.97) with high accuracy scores of 0.80. Based on the flood susceptibility maps, most of the northwest regions of the study area are more likely to flood because of low land areas, areas with a lower gradient slope, linear and concave shape curvature, high drainage density with high rainfall, more “water bodies,” “crops land,” and “built areas,” abundance on sea and surface water, and Quaternary types of soil feature and so on. The very high flood susceptibility class accounts for 18.2% of the study area, according to the RF-embedding model, whereas the high, moderate, low, and very low susceptibility classes were found at about 20.0%, 24.6%, 24.3%, and 12.9%, respectively. Conclusion: In comparison with other commonly used applied approaches, this research presents a novel modeling approach for flood susceptibility that integrates machine learning and geospatial data. It has been found to be stronger and more efficient, highly accurate, has good prediction performance, and is less biased. Overall, our research into machine learning-based solutions points in a positive path technologically and can serve as a reference manual for future research and applications for academic specialists and decision-makers.

Mitigating the flood disaster effects through the implementation of knowledge management: A systematic literature review

Climate change is global threat and the world is now facing its outcomes more than the last decades. Climate change has increased the frequency and intensity of flooding among other extreme weather events. The execution of knowledge management in different phases of disaster management can be effective in mitigating damages caused by floods to natural and human environments. The present study identified effective actions to implement the five stages of knowledge management in the stages before, during and after the flood event. For this purpose, a systematic review of sources and qualitative content analysis have been applied to evaluate the published research in four stages of items collection, descriptive analysis, selection and classification, and assessment. According to the findings, the pre-crisis stage requires 13 actions in knowledge creation, 20 actions in knowledge acquisition, 7 actions in knowledge organization and storage, 23 actions in knowledge sharing, and 20 actions in knowledge use and development. The crisis phase requires 12 actions in knowledge sharing and 10 actions in knowledge use. In the post-crisis stage, 5 actions in the knowledge sharing, 11 actions in the knowledge use, and 12 actions in the knowledge evaluation phases have been identified. To prevent and manage the flood crisis, knowledge improvement and management is an effective and necessary aspect that should be considered among the organizations and communities involved in emergency management.

Flood Risk Perception and Adaptation in Martapura River

Indonesian river basin towns face flood disasters that damage lives, infrastructure, and socioeconomic stability. Recent flood frequency and intensity have increased in the Martapura River settlements in Banjar Regency, South Kalimantan. This study examines flood risk perception and adaptation measures. The study analyzes flood risk perception, adaption strategies, and factors affecting them among residents. The 2023 mixed-method study combined quantitative surveys (n=358) and qualitative interviews (n=25) in five sub-districts over six months. The study found that previous flood experience was the best predictor of high-risk perception and adaptation strategies across geographic and socioeconomic boundaries. The adaption options ranged from structural changes to community-based efforts. Local government announcements and social media were primary sources, but environmental knowledge was low. Cultural linkages to the river and economic limits shaped adaptation. These findings highlight the necessity for personalized risk communication, improved early warning systems, and local expertise in modern flood control. The work adds to the knowledge on community resilience in flood-prone developing nations and informs policymakers and disaster managers. Future research should focus on long-term climate change adaptation techniques and novel ways to overcome economic barriers to individual and community flood resilience.

The research on narrow-valley city flood control mode based on hydrodynamic-hydrological coupling model

The flood processes and control methods in narrow-valley cities exhibit significant differences compared to other regions. Therefore, precise modeling and simulation of their flood processes is crucial. This study focuses on a typical narrow-valley city located in the Loess Plateau of northern China. A hydrodynamic-hydrological coupling model was constructed to simulate the floods, analyzing flood disaster characteristics as well as the effects and differences of various flood control measures in the narrow-valley city. The results indicate: (1) The hydrodynamic-hydrological coupling model proposed in this paper is highly appropriate for accurately simulating floods in narrow-valley cities. A hydrological model was used to generalize the hydrological process in hilly mountainous areas. While a hydrodynamic model was employed to conduct a detailed simulation of the river terrace, where the main urban area is located. After validation through two actual flood events, the model demonstrated satisfactory simulation performance. (2) The flood characteristics of narrow-valley cities differ from those of plain regions mainly because the terrain restricts the spread of floods once they surpass the embankments. This limitation leads to potentially greater inundation risks in such topographical features. Additionally, the elongated distribution of cities in these areas creates spatial disparities in flooding between upstream and downstream areas. (3) There are significant variations in the effectiveness of different flood control measures. Various flood control approaches were modeled and simulated in this paper. Their flood control capabilities were ranked in descending order as: reservoirs, flood diversion channels, and river channel dredging. (4) The combined flood control effect of multiple measures is notably superior to that of individual measures. Among them, the combination of reservoirs and flood diversion channels is optimal, as it regulates the inflow from a large upstream area. The conclusions drawn in this paper can provide a certain degree of reference and basis for the construction of flood models and flood control systems in narrow-valley cities.

Analysis dam failure and flood inundation mapping: a case study of Manbaa-Al-Ghozlan dam, Biskra, Algeria

Floods in all their form are considered a real threat to many regions of the world. There are various types of floods, including those caused by the collapse of dams, which must be predicted to mitigate their effects. One of the non-structural solutions for flood mitigation and control is drawing the dam failure hazard map using effective and accurate flood models, which are essential for flood disaster prevention and mitigation. The study aims at the delineation of the flood plain area caused by Manbaa-Al-Ghozlan dam failure in the city of Biskra (About 40 km downstream the dam) and the surrounding communities (the village of El Outaya). Two scenarios were considered and simulated using the HEC-RAS 2D (River Analysis System) software, the first one is related to the sudden collapse of the dam and the second linked to under-evaluations of the projected flood of the dam spillway. The results of the first scenario confirmed that there are no risks threatening the city of Biskra taking into account the water depth which reached 5 meters in the city of El Outaya. Conversely, the water depth varying between 16m and 40m in the city of El Outaya and can exceed 16 meters in the city of Biskra for the second scenario, and this performs as a great danger for the zone. This simulation allowed designing the dam failure hazards map, which showed the areas threatened by flood waves, and consequently required a plan of security measures to moderate as much as possible the consequences of a dam collapse.

Investigating the influence of residents' attitudes, perceptions of risk, and subjective norms on their willingness to engage in flood prevention efforts in Freetown, Sierra Leone

This study investigates the intricate relationships between flood disaster risk perception (RP), attitudes(ATT), subjective norms(SN), and participation in flood prevention activities(IPF), emphasizing the mediating roles of self-efficacy(SE) and bonding social capital(BSC). The research in Freetown, Sierra Leone, involved 702 participants, providing a diverse socio-demographic snapshot crucial for understanding community-based flood risk management. Data analysis revealed that demographic factors like age, gender, education, and income significantly influence flood risk perceptions and mitigation behaviors. Additionally, subjective norms were found to substantially impact both self-efficacy and bonding social capital, affecting participation in flood prevention activities. The study also explored the role of community dynamics and social norms in shaping risk perceptions and intentions to engage in flood risk prevention. It was observed that higher self-efficacy and more robust community bonds lead to increased participation in flood mitigation efforts. The findings offer valuable insights into flood prevention behavior's psychological and social drivers and highlight the importance of community-focused strategies in enhancing flood resilience. The study contributes to the broader understanding of flood risk management in urban settings, particularly in developing countries, and underscores the need for policies and practices that foster individual empowerment and collective community action.

Flood Disaster Risk Management In Manado City

Geological and hydrometeorological disasters are disasters that are often experienced by every region in Indonesia. Hydrometeorological disasters such as floods are influenced by strong westerly winds and world climate change. Apart from that, inappropriate land conversion is also one of the factors causing this disaster. One of the regions in Indonesia that is highly vulnerable to flood disasters is the city of Manado. Geographically, Manado City is surrounded by mountains and 5 large rivers that pass-through Manado City, namely the Tikala river and the Tondano river which merge in the Paal 2 area, the Malalayang river, the Sario River and the Bailang River. To overcome this disaster, it is necessary to carry out appropriate mitigation efforts to minimize the impact caused by the flood disaster in Manado City. The aim of this research is to analyze the risk level of flood disasters in Manado City and provide appropriate recommendations related to flood disaster mitigation in Manado City. This research uses analysis with quantitative descriptive research methods. The results of this research are that there are 3 classes of flood disaster risk levels in Manado City. In the high class there are 53 subdistricts, medium 2 subdistricts and low 32 subdistricts. Policy recommendations are made based on the level of risk by taking into account the main causal factors. Furthermore, it is recommended in every sub-district.

Increasing Community Preparedness In Facing Floods Disaster In Jakarta City

Flood disasters are the third cause of economic loss of all natural disasters throughout the world. Jakarta, as a waterfront city, is often hit by floods every year, especially during the rainy season. This research is focused on identifying and analyzing community responses and their relationship with their knowledge, preparedness and level of action towards disasters. This research is an exploratory case study research based on primary and secondary data. Research findings show that people in coastal areas have a high level of knowledge about floods (64%). This knowledge is high relative to the sum of their past experiences; However, they are less prepared (43%) because most of the residents are fishermen with low incomes so they don't have to make much effort to adapt to flood-affected houses. On the other hand, people in the interior, they lack knowledge (18%), but they have a good level of preparedness (24%) because of their relatively high-income level. Furthermore, both communities in coastal and inland areas had a high level of action due to high levels of knowledge and experience for coastal residents and good preparedness for inland residents.

Evolution of Flood Prediction and Forecasting Models for Flood Early Warning Systems: A Scoping Review

Floods are recognised as one of the most destructive and costliest natural disasters in the world, which impact the lives and livelihoods of millions of people. To tackle the risks associated with flood disasters, there is a need to think beyond structural interventions for flood protection and move to more non-structural ones, such as flood early warning systems (FEWSs). Firstly, this study aimed to uncover how flood forecasting models in the FEWSs have evolved over the past three decades, 1993 to 2023, and to identify challenges and unearth opportunities to assist in model selection for flood prediction. Secondly, the study aimed to assist in model selection and, in return, point to the data and other modelling components required to develop an operational flood early warning system with a focus on data-scarce regions. The scoping literature review (SLR) was carried out through a standardised procedure known as Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The SLR was conducted using the electronic databases Scopus and Web of Science (WoS) from 1993 until 2023. The results of the SLR found that between 1993 and 2010, time series models (TSMs) were the most dominant models in flood prediction and machine learning (ML) models, mostly artificial neural networks (ANNs), have been the most dominant models from 2011 to present. Additionally, the study found that coupling hydrological, hydraulic, and artificial neural networks (ANN) is the most used ensemble for flooding forecasting in FEWSs due to superior accuracy and ability to bring out uncertainties in the system. The study recognised that there is a challenge of ungauged and poorly gauged rainfall stations in developing countries. This leads to data-scarce situations where ML algorithms like ANNs are required to predict floods. On the other hand, there are opportunities to use Satellite Precipitation Products (SPP) to replace missing or poorly gauged rainfall stations. Finally, the study recommended that interdisciplinary, institutional, and multisectoral collaborations be embraced to bridge this gap so that knowledge is shared for a faster-paced advancement of flood early warning systems.

Assessing the IUCN global standard for nature-based solutions in riverine flood risk mitigation

The International Union for Conservation of Nature (IUCN) published their Global Standard for Nature-based Solutions (NbS) in an effort to further a common understanding and successful application of NbS. Our objective is to analyse the applicability of and considerations and advancements in using the IUCN Standard, as very few studies have examined and reflected on its actual application. As method, we applied the IUCN Standard to three case studies of river restoration projects with a focus on flood risk mitigation: (1) Eddleston Water Project, (2) “Room for the River” Deventer Project, and (3) Missouri River Levee Setback Project. Rather than evaluating the case studies itself, we evaluated the outcome to find the strong and weak points of the IUCN Standard. The gathered data (publicly accessible documents, conducted interviews with experts and stakeholders) was analysed and showed the role of the number of documents and interviews available. This determined the outcome of the IUCN assessment. The consultation of project experts has appeared to be an essential step in the data collection, while stakeholder interviews and field visits were less important, but did increase the degree of substantiation and the ease of data collection, respectively. Although restricted by a limited evaluation of flood risk mitigation studies, using the IUCN Standard for an ex-post assessment can provide credibility to project processes (e.g. stakeholder engagement and adaptive management), reveal project strengths and weaknesses, and provide opportunities for the comparison of projects. Hence, the IUCN Standard aptly evaluates process-based aspects of Nature-based Solutions for riverine flood risk mitigation.

A combined qualitative–quantitative method for adaptive configuration of urban flood mitigation measure

Inappropriately selecting flood mitigation measures can lead to ineffective governance and wastage of social resources. To address this issue, this research proposes a framework for adaptive configuration of urban flood mitigation measure. This framework aims to achieve a two-sided matching between the disaster reduction capacity of measures and the specific demands of the construction site. The framework was employed to the urban flood mitigation strategy in Longkungou drainage system, an optimal allocation scheme for addressing urban flood disasters under the design rainfall with 50-year return period was determined. The results revealed that green mitigation measures are primarily concentrated in the drainage system within the upstream catchment. Stormwater pumping stations are mainly situated at the drainage outlets and adjacent river channels in the study area. By reducing the water level in the river channel, these stations effectively prevent and manage flood volume resulting from river overflow. As a result of the flood mitigation strategy, the flood volume in the Longkungou drainage system was reduced by 591,186 m3, representing a peak total flood volume reduction rate of 42.44%. The severity of flood disasters in each sub-catchment has significantly diminished, indicating that the flood mitigation strategy has successfully achieved the desired improvement.

Ecoliteracy Learning Design with Augmented Reality-Based SETS Approach for Flood Disaster Education

Ecoliteracy Learning Design with Augmented Reality-Based SETS Approach for Flood Disaster Education

Identifying the centers of geographical public opinions in flood disasters based on improved conditional random field and focus theory

Location entities in social media data are helpful references for characterizing floods during the flood lifecycle; thus, accurately identifying location entities from social media data is a primary research topic. However, most researchers have used all locations identified from social media data to characterize floods, ignoring the characteristics of network public opinion, which represents a concentrated expression of online users’ opinions. Therefore, this study introduces the use of focus theory to define location entities. The location entities with focus attributes are defined as centers of geographical public opinion in flood disasters (CGeoPOFDs). In turn, these location entities can be used to characterize floods realistically. The model employs the front-back dynamic window and the semantic features of geographical location to improve the Conditional Random Field and applies correction functions to recall location labels and location entities. The study then applies the focus determination method to assign focus attributes to location entities and explores a method for identifying CGeoPOFDs. According to the results, the improved method and correction functions improve the recognition effect of location labels and location entities, confirming the success of using the focus determination method for focus semantic screening of all location entities. These results prove the authenticity of the CGeoPOFDs and the feasibility of the CGeoPOFDs identification method. This study opens up a new perspective on the use of social media data to characterize floods.

Walking With/In Place: The Walking Turn In Socially Engaged Public Art From Aotearoa New Zealand

This article first suggests that there has been a “Walking Turn” in contemporary socially engaged public art and then considers critically how these practices might be best framed and understood in the context of Aotearoa. There has been a multitude of walking-based artworks in Aotearoa since the second half of the 2010s to the present. In the wake of the global pandemic and natural flooding and cyclone disasters in Aotearoa that soon followed, “going out for a walk” has been repeatedly problematized and reframed for most citizens. This article seeks to establish a theoretical framework that is responsive to Indigenous as well as posthumanist discourse, through which we might best consider walking art practices in Aotearoa. To do this, the concept of walking “with/in” place is employed as an entry point. Drawing on research interviews, a number of culturally informed concepts related to walking are examined alongside notions of countermapping and countermonument. The artwork analysis considers the decolonial, counterneoliberal, and regenerative principles underlining these practices. The artworks discussed engage in histories and contemporary politics of place and can be characterized by quietude, humility, and hyperlocality.

A novel AI-based model for real-time flooding image recognition using super-resolution generative adversarial network

Intensified climate change in recent years has had a global impact, leading to increased precipitation events of short duration and high intensity. This phenomenon poses a severe challenge to urban and underground infrastructure. Accurate detection and location of floodplains and water bodies are essential to ensure informed decision-making and implement proactive measures to minimize risks and losses. Therefore, an automated and efficient flood image detection system is the need of the hour. This study proposes a deep learning–based flood detection system. Images taken by surveillance cameras at intersections are used as input data, making the system well-suited to urban applications. Data augmentation techniques are used to improve the model performance. We demonstrated the practicality of this model by applying it to street surveillance images taken in Taiwan. The developed model quickly and successfully identified the extent and location of flooding with high precision and reliability. The developed model can be used to provide valuable insights for flood management and disaster management agencies. The test findings obtained from this study demonstrate the superior performance of the DeepLabv3 + model compared to the Mask R-convolutional neural network model, which was further enhanced using a super-resolution generative adversarial network. The model achieved remarkable precision with a precision metric score of 84 %, which is also complemented by a recall rate of 91 %. Most notably, the mean Intersection over Union (mIoU) metric reached an impressive accuracy level of 85.8 %. The results of this study highlight the importance of developing advanced flood imagery detection models aiming to considerably reduce the risks and losses incurred by flooding. The application of such a flood image detection system could help increase the ability of a city to cope with flood events caused by climate change.

Quantification of the flood mitigation ecosystem service by coupling hydrological and hydrodynamic models

Flood mitigation service provides crucial information for reducing flood disasters and assessing ecosystem capacities by quantifying how much damage is reduced and how many benefiting areas are protected during flood events. However, there remains a gap in the full-process quantification, which results in less precise simulation outcomes. In this study, we introduce a novel methodology to accurately quantify the flood mitigation service of ecosystems by coupling hydrological and hydrodynamic models. We utilized the Hydrological Simulation Program-Fortran (HSPF) model to simulate peak flow and flood volume and then used these data as inputs for the Environmental Fluid Dynamics Code (EFDC) hydrodynamic model to simulate the spatial extent and depth of flood inundation. The contribution and capacity of the ecosystem are reflected through the reduction in peak flow, flood volume, and inundation areas. We used the Nandu Basin flood event in October 2010 as a case study to illustrate our approach, comparing our assessment results with those simulated by the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Height Above Nearest Drainage (HAND) model. The results demonstrate that coupling the HSPF model (R2 = 0.93) with the EFDC model (overlap ratio = 83.71 %) allows for precise quantification of flood mitigation service. The process-based hydrological and hydrodynamic models show a high correlation with the simpler and faster InVEST and HAND model simulations, with the full-process models reducing relative errors by 7.66 % and 5.25 % respectively. This study offers a promising approach for accurately and comprehensively assessing flood mitigation ecosystem service and provides a basis for model selection.