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Floods have been identified as the most frequent and threatening disaster in Sri Lanka amidst an increasing trend of natural and man-made disasters in the world. Subject experts state that disaster risk management should be based on the results of risk assessments, but flood risk management in Sri Lanka is seemingly not based on community-level flood risk assessments. Accordingly, the purpose of this paper is to introduce a community-level flood risk assessment method to the local context of Sri Lanka.

The sample (n = 425) for the study was selected using the stratified random sampling method, and the Deduru Oya basin was selected as the study area. The risk assessment model introduced by Bollin et al. (2003) was used for the current study, but with some modifications. Accordingly, 16 variables were selected for the risk assessment. Descriptive data analysis methods were used in the study.

Community-level flood risk assessment method was introduced. Variable index, flood risk index and flood risk map were developed for the study area. The Grama Niladari Divisions (GNDs) were grouped into five categories from very high risk to very low risk. The GNDs named Wirakumandaluwa, Thimbilla, Deduru Oya, Bangadeniya and Elivitiya were ranked as the most flood-risk GNDs, respectively.

This paper produces a flood risk assessment method for the local context. Flood risk in the study area was assessed based on people’s perceptions. Accordingly, the flood risk index and flood risk map for the study area were developed based on the empirical data. GNDs were ranked based on the flood risk index.

Flood preparedness and response from the perspective of community engagement mechanisms have been studied in scholarly articles. However, the differences in flood mitigation may expose social and behavioural challenges to learn from. This study aimed to demonstrate how text mining can be applied in prioritising existing contexts in community-based and government flood mitigation and management strategies.

This investigation mined the semantics researchers ascribed to flood disasters and community responses from 2001 to 2022 peer-reviewed publications. Text mining was used to derive frequently used terms from over 15 publications in the Scopus database and Google Scholar search engine after an initial output of 268 peer-reviewed publications. The text-mining process applied the topic modelling analyses on the 15 publications using the R studio application.

Topic modelling applied through text mining clustered four (4) themes. The themes that emerged from the topic modelling process were building adaptation to flooding, climate change and resilient communities, urban infrastructure and community preparedness and research output for flood risk and community response. The themes were supported with geographical flood risk and community mitigation contexts from the USA, India and Nigeria to provide a broader perspective.

This study exposed the deficiency of “communication, teamwork, responsibility and lessons” as focal themes of flood disaster management and response research. The divergence in flood mitigation in developing nations as compared with developed nations can be bridged through improved government policies, technologies and community engagement.

In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management.

This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed.

The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making.

The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

Despite the present focus on improving the resilience of homes to flooding in UK flood risk management policy and strategy, a general measurement framework for determining levels of flood resilience in UK homes does not exist. In light of this, the aim of this study was to develop a means to evaluate the levels of resilience in flood-prone homes from the perspective of homeowners'.

A quantitative research methodology was employed, with empirical data obtained through a postal survey of homeowners who had experienced flooding. The responses received were then analysed using a combination of statistical techniques including agreement/reliability tests and multiple regression to develop a model of flood resilience.

A predictive model was developed that allows the resilience of a property to be quantified and measured as perceived by homeowners. The findings indicate that the main factors found to influence the level of flood resilience were: property type (PT), presence of cellar/basement (C/B), property wall type (PWT), property ground floor type (PGFT), kitchen unit type (KU), flood experience (FE), flood source (FS) and flood risk level (FRL).

The resulting model provides unique insights into resilience levels to the benefit of a range of stakeholders including policy makers (such as Defra/Environment Agency), Local Authority flood teams, property professionals, housing associations and homeowners. As a result, homeowners will be in a better position to determine which interventions should be prioritised to ensure better flood protection.

This is the first study of its kind to have rigorously quantified the level of flood resilience for individual homes. This study has quantified the effectiveness of individual resilience measures to derive the first reliable means to measure the overall levels of resilience at the individual property level. This is regarded as a significant contribution to the study of flood risk management through the quantification of resilience within individual UK homes, enabling the prioritisation of interventions and the overall monitoring of resilience.

The purpose of the paper is to predict mapping of areas vulnerable to flooding in the Ourika watershed in the High Atlas of Morocco with the aim of providing a useful tool capable of helping in the mitigation and management of floods in the associated region, as well as Morocco as a whole.

Four machine learning (ML) algorithms including k-nearest neighbors (KNN), artificial neural network, random forest (RF) and x-gradient boost (XGB) are adopted for modeling. Additionally, 16 predictors divided into categorical and numerical variables are used as inputs for modeling.

The results showed that RF and XGB were the best performing algorithms, with AUC scores of 99.1 and 99.2%, respectively. Conversely, KNN had the lowest predictive power, scoring 94.4%. Overall, the algorithms predicted that over 60% of the watershed was in the very low flood risk class, while the high flood risk class accounted for less than 15% of the area.

There are limited, if not non-existent studies on modeling using AI tools including ML in the region in predictive modeling of flooding, making this study intriguing.

Understanding the technical and socioeconomic dimensions to resilience is core to making a business case for property-level flood risk adaptation. The study investigates the socioeconomic factors that impact the technical dimensions to property-level flood risk adaptation, and whether there is a typology of households adopting similar adaptation measures in Port Harcourt.

Exploratory and inferential statistical analysis of data collated from 407 questionnaires was carried out. Using chi-square analysis, significant bivariate associations were sought between the level of uptake of different categories of property level adaptation and the socioeconomic characteristics of households. A two-step cluster analysis was used to explore discernible patterns of households implementing similar adaptation measures. Logistic regression analysis was further used to evaluate the extent to which socioeconomic parameters impact residents' willingness to undertake adaptation measures given the option of relocation.

The chi-square analysis highlighted a lack of significant association between some socioeconomic parameters and the uptake of individual adaptation measures. The regression analysis however showed that the socioeconomic parameters exert varying degrees of influence on the residents' willingness to undertake adaptation measures. Two homogeneous groups of residents with similar socioeconomic characteristics were identified via the cluster analysis but did not translate into strongly discernible adaptation differences/patterns.

The study shows that although socioeconomic parameters to some extent underlie the technical dimensions to flood resilience, there is no distinctive typology of households in Port Harcourt adopting a similar combination of measures.

The study offers insights into understanding property-level flood risk adaptation responses within the context of the developing world.

This paper aims to provide scholars with a deep understanding of the field through the identification of strengths and weaknesses in the literature and support decision-makers in the development of new practices in local risk management based on scientific data. The specific question in this review asks: what are the drivers and barriers to local risk management?

This paper provides an overview of the scientific literature produce over the past 20 years of the divers and barriers to local risk management. This paper presents a scoping review of peer-reviewed articles published between 2000 and 2019 inclusively in the fields of public policy and public administration.

This paper makes three main observations regarding the state of the literature. First, this paper finds that scholars mainly focus on single risk and certain regions of the world. Second, there is multiple approached used by the literature to study risk management at the local level. Third, little attention is given to the political context in which local risk management takes place.

This paper is a complete literature review of more than 500 peer-reviewed articles published in academic journals regarding risk prevention policies over the past two decades. This paper analyzed the main findings of the current literature to provide a general view of the scholarship and improve the collective understanding of risk management at the local level by providing future research avenues.

A comprehensive understanding of the determining factors and implications of the frameworks for appreciating the relationships between climate risks and cultural heritage remains deficient. To address the gap, the review analysed literature on the management of climate risk in cultural heritage. The review examines the strengths and weaknesses of climate risk management (CRM) frameworks and attendant implications for the conservation of cultural heritage.

The study adopted a two-phased systematic review procedure. In the first phase, the authors reviewed related publications published between 2017 and 2021 in Scopus and Google Scholar. Key reports published by organisations such as the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and International Council on Monuments and Sites (ICOMOS) were identified and included in Phase Two to further understand approaches to CRM in cultural heritage.

Results established the changes in trend and interactions between factors influencing the adoption of CRM frameworks, including methods and tools for CRM. There is also increasing interest in adopting quantitative and qualitative methods using highly technical equipment and software to assess climate risks to cultural heritage assets. However, climate risk information is largely collected at the national and regional levels rather than at the cultural heritage asset.

The review establishes increasing implementation of CRM frameworks across national boundaries at place level using high-level technical skills and knowledge, which are rare amongst local organisations and professionals involved in cultural heritage management.

The review established the need for multi-sectoral, bottom-up and place-based approaches to improve the identification of climate risks and decision-making processes for climate change adaptation.

This paper aims to quantitively assess the resilience of residential properties to urban flooding in Port Harcourt, Nigeria, and assess whether they vary at spatially aggregated scales relative to the level of flood exposure.

The study synthesizes theoretical constructs/indicators for quantifying property level resilience, as a basis for measuring resilience. Using a two-stage purposive/stratified randomized sampling approach, 407 questionnaires were sent out to residents of 25 flood-prone areas, to solicit information on the resilience constructs as indicated by the adaptation behaviors of individual households and their property attributes. A principal component analysis approach is used as a mechanism for weighting the indicators, based on which aggregated spatial-scale resilience indices were computed for the 25 sampled areas relative to their levels of flood exposure.

Area 11 located in the moderate flood zone has the lowest resilience index, while Area 20 located in the high flood zone has the highest resilience index. The resilience indices for the low, moderate and high flood zone show only minimal and statistically insignificant differences indicating maladaptation even with incremental levels of flood exposure.

The approach to resilience measurement exemplifies a reproducible lens through which the concept of “living with floods” can be holistically assessed at the property level while highlighting the nexus of the social and technical dimensions.

The study moves beyond theoretical conceptualization, to empirically quantify the complex concept of property-level flood resilience.

The purpose of this paper is to explore flood-prone area residents' preferences of flood-resilient housing technologies (HTs), to understand the factors influencing their choices. Flood-resilient HTs can reduce damage and disruption at a household level, particularly in areas where large-scale community schemes are not available or feasible. People’s perception of floods and their preferences of flood-resilient HTs are among many very important factors influencing the adoption of these technologies. Therefore, these perceptions and preferences must be well understood before implementation of these technologies can occur. However, studies on these two important factors are lacking in literature, particularly in the sub-Saharan African context.

Nigerian residents’ preferences of flood-resilient HTs were explored by focusing on five frequently flooded areas around the Niger and Benue river basins in Kogi State, Nigeria. Thirty-eight chat, video and voice call interviews were conducted with participants across five case study areas: Lokoja, Idah, Bassa, Ajaokuta and Koton Karifi. The interviews, informed through an illustrated brochure, covered residents’ experiences and perceptions of floods. This was done to gain an understanding of the factors influencing the choice of flood-resilient HTs adopted and those preferred.

This study confirms that residents in these five focus areas show similar characteristics to other floodplain residents as encapsulated in protection motivation theory. The flood-resilient HTs discussed in this study include flood-avoidance, flood-recoverability and flood-resistance strategies, as well as neighbourhood-scale approaches. Flood-resistance and flood-recoverability strategies rated highly in terms of suitability and envisaged efficiency in mitigating flooding in Kogi State. Although the measures were mostly agreed to be potentially effective and successful on a household scale, there were concerns as to flood mitigation on a neighbourhood scale.

Pre-existing flood-resilient HTs were not extensively discussed in the literature review but were included to have a sense of the participants’ mitigation behaviour, as well as their potential to adopt (or not) new measures after adopting previous ones.

The results provide supporting evidence of the factors influencing the choice of and/or intention to adopt flood-resilient HTs, highlighted in literature. Results also contribute to literature by providing further insight into flood-resilient measures already adopted by residents, as well as their preferred HTs from the options presented. The implications of these findings and methodological considerations in this research are fully discussed in this paper.