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Shadegan County as a wetland area was selected because of its susceptibility to flooding hazards and inundation. The purpose of this paper is to analyze flooding hazard based on the analytical hierarchy process methodology.

The eight influencing factors (slope, distance from wetland, distance from river, drainage density, elevation, curve number, population density and vegetation density) were considered for flood mapping within the Shadegan County using analytical hierarchical process, geographical information system and remote sensing. The validation of the map was conducted based on the comparison of the historical flood inundation of April 21, 2019.

The results showed that around 32.65% of the area was under high to very high hazard zones, whereas 44.60% accounted for moderate and 22.75% for very low to the low probability of flooding. The distance from Shadegan Wetland has been gained high value and most of the hazardous areas located around this wetland. Finally, the observed flood density in the different susceptibility zones for the very high, high, moderate, low and very low susceptible zones were 0.35, 0.22, 0.15, 0.19, and 0.14, respectively.

To the best of the authors’ knowledge, the flood susceptibility map developed here is one of the first studies in a built wetland area which is affected by anthropogenic factors. The flood zonation map along with management and restoration of wetland can be best approaches to reduce the impacts of floods.

The purpose of this paper is to report research testing scales developed from a combination of vested interest (VI) theory and the extended parallel process model of fear appeals. The scales were created to measure variables specified by an expanded model of VI: certainty, salience, immediacy, self-efficacy, response-efficacy, and susceptibility.

A survey was designed with subscales for each element and combined with additional disaster and risk perception variables. Survey data were collected from two populations in the US state of Oklahoma. Results from scale development and regression analyses are reported.

Results show that the scales are robust and flexible to contextual modification. The scales return good to excellent reliabilities, providing evidence that the variables articulated by VI theory predict perceived salience and perceived preparedness.

This study adds to the research pointing to the efficacy of VI theory in providing insight into the perceptual barriers to preparedness. These results demonstrate that perceived vestedness can be a valuable tool in crafting messages to inform audiences of risks and motivate them to prepare.

These results can facilitate the creation of more effective hazard and risk messages. Related research shows households that are prepared for natural and manmade hazards enjoy higher rates of survivability and lower levels of consequences.

This paper presents new results concerning perceived vestedness and the utility of the scales. The research should be of value to practitioners and policymakers concerned with motivating public audiences to prepare for natural and manmade hazards.

This paper aims to examine and compare extant literature on the application of multi-criteria decision-making (MCDM) techniques in humanitarian operations (HOs) and humanitarian supply chains (HSCs). It identifies the status of existing research in the field and suggests a roadmap for academicians to undertake further research in HOs and HSCs using MCDM techniques.

The paper systematically reviews the research on MCDM applications in HO and HSC domains from 2011 to 2022, as the field gained traction post-2004 Indian Ocean Tsunami phenomena. In the first step, an exhaustive search for journal articles is conducted using 48 keyword searches. To ensure quality, only those articles published in journals featuring in the first quartile of the Scimago Journal Ranking were selected. A total of 103 peer-reviewed articles were selected for the review and then segregated into different categories for analysis.

The paper highlights insufficient high-quality research in HOs that utilizes MCDM methods. It proposes a roadmap for scholars to enhance the research outcomes by advocating adopting mixed methods. The analysis of various studies revealed a notable absence of contextual reference. A contextual mind map specific to HOs has been developed to assist future research endeavors. This resource can guide researchers in determining the appropriate contextual framework for their studies.

This paper will help practitioners understand the research carried out in the field. The aspiring researchers will identify the gap in the extant research and work on future research directions.

To the best of the authors’ knowledge, this is the first literature review on applying MCDM in HOs and HSCs. It summarises the current status and proposes future research directions.

In October 2020, Vietnam was repeatedly hit by large storms, including Linfa, Nangka, Saudel and Molave, causing heavy rains and whirlwinds in the Central provinces of Vietnam. The heavy rain led to severe flooding in many localities. The water levels on major rivers broke records of historical flood events in 1950, 1979, 1999, 2007, 2010 and 2016. In response, this paper aims to quantify the impacts of 2020 flooding to support flood risk management activities and the relief agencies that can use the analysis.

This study demonstrates an approach to quickly map flood impacts on population, schools, health-care facilities, agriculture, transportation and business facilities and assess flood risks using available data and spatial analysis techniques.

The results show that all districts of Quang Binh were affected by the event, in which 1,014 residential areas, 70 schools, 13 health-care facilities, 32,558 ha of agriculture lands, 402 km road length, 29 km railway, 35 bridges on roads and 239 business facilities were exposed within flooded areas.

This study is limited to direct or tangible impacts, including flooded residential areas, schools, health-care facilities, agriculture land categories, road networks and business facilities. Indirect or intangible impacts such as health, flood pollution and business disruption should be considered in further studies.

These detailed impact maps can support decision-makers and local authorities in implementing recovery activities, allocating relief and devoting human resources and developing flood risk management action plans and land-use planning in the future.

This study investigates the context of flood impacts on population, schools, health-care facilities, agriculture, transportation and business facilities. Based on this research, decision-makers can better understand how to support affected communities and target the most at risk people with interventions.

This paper presents a framework to quantify the impacts of the 2020 extreme flood event using available data and spatial analysis techniques in support of flood risk management activities.

The purpose of this paper is to study an articulated warning system that provides information about the heritage at risk and encourages a dialogue between the heritage sector, civil defense agencies and local communities.

The databases from the National Heritage Institute, National Civil Defense, National Geological Service and National Early Warning System were investigated and the local community provided input which helped form a participatory risk mapping strategy for a warning system in the heritage sector.

There is little knowledge of the Brazilian heritage that is at risk and a lack of coordination between the cultural heritage and DRR sectors. This means that there is a need to organize the geo-referenced databases so that information can be shared and the public provided with broader access. As a result, there can be a greater production, dissemination and application of knowledge to help protect the cultural heritage.

The findings can be included in the debate about the importance of framing disaster risk management (DRM) policies in the Brazilian heritage sector.

The findings and maps of the case study in the town of São Luiz do Paraitinga involve the heritage sector, civil defense agencies and local people and can be used for disaster risk preparedness.

A DRM program is being formulated in Brazil. However, the kind of strategy needed to incorporate the heritage sector in this program stills needs to be planned, and the knowledge of the cultural heritage at risk is a key factor when faced with this new social and scientific challenge.

Tropical storms Urduja and Vinta battered the Philippines in December 2017. Despite advances in disaster risk reduction efforts of the country, the twin December storms caused numerous deaths in the Visayas and Mindanao regions. Analysis of these events shows that alerts raised during the Pre-Disaster Risk Assessment (PDRA) for both storms were largely ineffective because they were too broad and general calling for forced evacuations in too many provinces. Repeated multiple and general warnings that usually do not end up in floods or landslides, desensitize people and result in the cry-wolf effect where communities do not respond with urgency when needed. It was unlike the previous execution of PDRA from 2014 to early 2017 by the National Disaster Risk Reduction and Management Council (NDRRMC), which averted mass loss of lives in many severely impacted areas because of hazard-specific, area-focused and time-bound warnings. PDRA must reinstate specific calls, where mayors of communities are informed by phone hours in advance of imminent danger to prompt and ensure immediate action. Mainstreaming Climate Change Adaptation and Disaster Risk Reduction information using probabilistic (multi-scenario) hazard maps is also necessary for an effective early warning system to elicit appropriate response from the community. The paper aims to discuss these issues.

Methods of early warning through the PDRA of the National Disaster Mitigation and Management Council (NDRRMC) of the Philippines during tropical storm Urduja and Typhoon Vinta were assessed in this study and compared to the previous PDRA system from 2014 to early 2017.

It was found out that the numerous casualties were due to inadequate warning issued during the approach of the tropical cyclones. During an impending hazard, warnings must be accurate, reliable, understandable and timely. Despite the availability of maps that identified safe zones for different communities, warnings raised during the PDRA for both tropical cyclones were deemed too general calling for evacuations of whole provinces. As such, not all communities were evacuated in a timely manner because of failure in the key elements of an effective early warning system.

To avoid future disasters from happening, it is recommended that the PDRA reinstate its hazards-specific, area-focused and time-bound warnings. Similarly, to increase the resilience of communities, more work on mainstreaming of Climate Change Adaptation and Disaster Risk and Vulnerability Reduction systems for communities must be done as well. Learning from the lessons of these previous disasters will enable communities, their leaders and every stakeholder, not to repeat the same mistakes in the future.

Floods are considered as one of the most lethal natural disasters having the potential to cause havoc to entire communities. Pakistan is the land of wide topographic and climatic variations which make it vulnerable to floods. The purpose of this paper is to identify flood susceptible zones in the Panjkora Basin using frequency ratio model.

A total of seven parameters or flood conditioning factors were considered, and weights were assigned according to the frequency ratio technique. For the preparation of layers, satellite imageries and digital elevation model data were used. Frequency ratio was calculated using correlation between these parameters and flood. Flood susceptibility index map was divided into five zones through quantile method in ArcMap.

Findings of the study reveal that near half of the area (43%) is located in the very high susceptible zone, while only 20% area is classified as low to very low susceptible.

This paper is entirely based on original research. The approach used in this study has not been applied to the study area before.

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.

Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.

This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.

Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.

The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

Although the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. Many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM).

The approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems.

The use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM; hence, the decision-maker should consider using more quality measures if the objective is to preserve the original PCM characteristics.

For the first time, a many-objective approach reduces the CR to consistent levels with the ability to consider one or more quality measures and allows the decision-maker to adjust each pairwise comparison individually.