Search results

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.

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

This study aims to explain the existing adaptation practices in an urbanized sub-region in the lower Chao Phraya River basin (CPRB) across different scales and dimensions. It offers an overview of water hazards in urban areas along the river basin to discover ways to deal with and recover from hazards via understanding the implications of existing and potential practice for the mitigation of hydrological hazards.

First, this study collected current adaptation strategies and measures from interview, focus group discussion, workshop organization, etc. to get the current adaptation strategies/measures for the whole CPRB and each specific area. Second, this study identified a set of criteria for evaluation from review of current publications and official reports. Then, the current adaptation strategies/measures were examined through a set of criteria to obtain the current situation of existing practices. Finally, analysis of key challenges and opportunities was done to propose supporting guidelines to reduce hydrological risks and incorporate further adaptation measures needed to boost resilience in the area.

Adaptation methods should focus on mixed adaptation, which integrates structural, social, organizational and natural adaptation, and to develop multi-dimensional collaboration. The adaption strategy has restricted the usage of some technologies and technical know-how, particularly in the area of climate change. As a result, intentional adaptation to become more inventive is required, to reduce hazards and improve disaster-response capacity. The various adaptation measures should be more integrated or more adaptive and to achieve greater cohesion and mutual benefit of individual measures, such as community-based adaptation or community-driven slum upgrading.

Hydrological risks are wreaking havoc on social, economic and environmental elements, particularly river flood, flash flood and drought in the Asia-Pacific region. Twenty-two existing adaptation options were evaluated with evaluation criteria such as scales of risks/impacts reduction, benefits of environmental and socio-economic and institutional aspects. The findings highlight the current situation of existing practices, key challenges and opportunities, which emphasized on natural-based solutions, raising knowledge and awareness and lessons learned on adaptation of hydrological risks. The existing adaptation measures will be suggested as supporting guidelines and master plans to minimize the hydrological risks.

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.

Evaluating the implementation of environmental management plans (EMPs) in highway construction projects is essential to avoid climate change. Public evaluations can help ensure that the EMP is implemented correctly and efficiently. To allow public evaluation of EMP implementations, this study aims to investigate performance indicators (PIs) for assessing EMP implementation in highway construction projects. To that end, the study objectives are to compare the critical PIs between environment auditors (EAs) and environment officers (EOs) and among the main project stakeholders (i.e. clients, contractors and consultants), create components for the critical PIs and assess the efficiency of the components.

The paper identified 39 PIs from interviews with environmental professionals and a systematic literature review. Then a questionnaire survey was developed based on the PIs and sent to EAs and EOs. The data were analyzed via mean score ranking, normalization, agreement analysis, factor analysis and fuzzy synthetic evaluation (FSE).

The analyses revealed 21 critical PIs for assessing EMP implementation in highway construction projects. Also, the critical PIs can be grouped into four components: ecological, pollution, public safety and ecological. Finally, the overall importance of the critical PIs from the FSE is between important and very important.

To the best of the authors’ knowledge, this paper is the first-of-its-kind study on the critical PIs for assessing EMP implementation in highway construction projects.

Climate change is a problem that concerns all of us. Despite the information produced by organizations such as the Expert Team on Climate Change Detection and Indices and the United Nations, only a few cities have been planned taking into account the climate changes indices. This paper aims to study climatic variations, how climate conditions might change in the future and how these changes will affect the activities and living conditions in cities, specifically focusing on Mexico city.

In this approach, two distinct machine learning regression models, k-Nearest Neighbors and Support Vector Regression, were used to predict variations in climate change indices within select urban areas of Mexico city. The calculated indices are based on maximum, minimum and average temperature data collected from the National Water Commission in Mexico and the Scientific Research Center of Ensenada. The methodology involves pre-processing temperature data to create a training data set for regression algorithms. It then computes predictions for each temperature parameter and ultimately assesses the performance of these algorithms based on precision metrics scores.

This paper combines a geospatial perspective with computational tools and machine learning algorithms. Among the two regression algorithms used, it was observed that k-Nearest Neighbors produced superior results, achieving an R² score of 0.99, in contrast to Support Vector Regression, which yielded an R² score of 0.74.

The full potential of machine learning algorithms has not been fully harnessed for predicting climate indices. This paper also identifies the strengths and weaknesses of each algorithm and how the generated estimations can then be considered in the decision-making process.

Safe-to-fail (SF) is an emerging resilient design approach that has the potential to minimize the severity of flood damages. The purpose of this study is to explore the SF design strategies to reduce flood disaster damages in US coastal cities. Therefore, this study addresses two research questions: identifying the most suitable SF criteria and flood solution alternatives for coastal cities from industry professionals’ perspective; and investigating the controlling factors that influence the AEC students’ interest to learn about SF concepts through the curricula.

This study used the analytical hierarchy process to evaluate the SF criteria and flood solutions where data were collected through surveying 29 Department of Transportation professionals from different states. In addition, the study adopted a quantitative methodology by surveying 55 versed participants who reside in a coastal area and have coastal flood experiences. The data analysis included ordinal probit regression and descriptive analysis.

The results suggest that robustness is the highest weighted criterion for implementing SF design in coastal cities. The results demonstrated that ecosystem restoration is the highest-ranked SF flood solution followed by green infrastructure. Moreover, the results highlighted that age, duration spent in the program and prior knowledge of SF are significantly related to AEC students’ interest to learn this concept.

SF design anticipates failures while designing infrastructures thus minimizing failure consequences due to flood disasters. The findings can facilitate the implementation of the SF design concept during the construction of new infrastructures in coastal cities as well as educate the future workforces to contribute to developing resilient built environments.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.

One of the crucial elements of addressing global climate challenges through urban tourism is the continuing existence of urban forests. The reasoning is that the ecological attraction of urban forests can impact visitors’ intention to conduct pro-environmental behavior, including low-carbon actions. Thus, more visitors to urban forests will positively affect enhancing the quality of the urban environment. However, the extent to which ecological attraction can influence pro-environmental behavior warrants further investigation due to the complexity of psychosocial factors that impact behavioral intention. The main objective of this research is to examine the effects of the ecological attractiveness of urban forests on the pro-environmental behavior of visitors by exploring motivation, ecological experience, perceived value and knowledge as mediators. Moreover, whether the nature of the urban forest and facilities attract visitors simultaneously is also studied.

Data were collected from 615 respondents who visited three urban forests in Bandung, the second-most populous city in Indonesia, by five-point Likert questionnaires. As an analytical tool, SEM PLS was applied to establish the effect of the ecological performance of the urban forest on the increase in environmentally conscious behavior among urban forest visitors.

The findings demonstrate that the attractiveness of an urban forest affects the growth of environmentally responsible behaviors. Nonetheless, the attractiveness of urban forests is dictated more by their infrastructure than their ecological function. On the contrary, the visitors’ knowledge level can improve their motivation, environmental experience and perceived environmental value. These findings show the significance of developing educational programs with an emphasis on the experience of the visitors so that their ecological performance can contribute to improved low-carbon behavior. In conclusion, this work contributes to the management of sustainable urban tourism.

This work also has some limitations. First, the medium R-square on intention behavior to low-carbon action suggests investigating other influential factors to produce a more robust conscious behavior. Mkono and Hughes (2020) mention that many complex factors that cause positive intention do not necessarily lead to environmental action. Thus, many psychosocial variables need to be explored in different models. Second, the convenient sampling used here does not represent the whole population, making generalization difficult. Thus, further work needs to apply more rigorous sampling techniques to validate the findings. Further investigations may also need to be conducted in other urban forests in another Asian country with a similar and different social context for benchmarking, as this study found that the type of attractive urban forest design is a more dense forest, which differs from other studies based in Europe. Exploring more influencing behavioral factors of pro-environmental action in the model is also suggested. Thus, we could contribute more to support recreational activities in urban forests.

As an implication for planning an urban forest to increase its recreational function, the authors illustrate the importance of producing educational programs. Although the improved knowledge of visitors has been shown to strengthen their commitment to perform pro-environmental actions, the mediating role of motivation, experience and perceived value reveals that some activities are required to achieve visitor motivation to actual behavior. Consequently, designing an urban forest requires not only the enhancement of eco-attractions and artificial elements for the convenience of visitors but also the development of an environmental education program that can improve visitors’ environmental experience and perception of ecological value. The designed educational program may use an experiential education approach incorporating objective knowledge of Earth’s current state. The urban forest education program must encourage visitors’ connection and participation with nature. Moreover, knowledge and information about Earth’s environmental quality can increase visitors’ perceived value, ensuring that their activities in the urban forest contribute to improved health, environmental quality and social environment. Thus, with well-managed and provided education, they are encouraged to adopt low-carbon action because it complements their contribution to a better quality environment.

The theoretical contribution of this research is generated through the role of urban forest attractiveness in the intention to conduct low-carbon action, which influences solutions to existing urban environmental problems. This work exhibits that both ecological attractiveness and attractiveness of artificial elements in urban forests can attract visitors and subsequently boost their outdoor recreation motivation, ecological experience and perceived value and then turn them to boost their intention to conduct low-carbon action. The physical characteristics of a site are behavioral stimuli that can increase a person’s motivation, experience and perception of the value of the environment, thereby increasing their intention to engage in low-carbon actions. This environment behavioral construction is fundamental in understanding that urban forests offer ecological benefits and influence the social quality of urban communities. Nevertheless, without visitor activity, urban forests are merely physical entities that become increasingly demanding to maintain. Due to this, an urban forest that is socially active and has an influence on promoting environmentally conscious behavior is needed, and its presence is becoming ever more crucial. This work shows the significance of integrating psychosocial approaches into managing tourism in urban forests.