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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.

Built environments are highly vulnerable to climatic disasters such as extreme floods, droughts and storms. Inaccurate decisions in adopting emerging construction technologies can result in missed opportunities to improve the resilience of built environments. Therefore, understanding the effectiveness of emerging construction technologies in improving built environment resilience can help in making better strategic decisions at the national and organizational levels. This study aims to evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience.

A list of Construction 4.0 technologies was adopted from a national strategic plan. Then, the data were collected using the fuzzy technique for order preference by similarity to ideal solution technique from selected built environment experts to determine the relative effectiveness of Construction 4.0 technologies in improving built environment resilience.

Six Construction 4.0 technologies are critical in improving built environment resilience (in rank order): building information modeling, autonomous construction, advanced building materials, big data and predictive analytics, internet of Things and prefabrication and modular construction. In addition, adopting Construction 4.0 technologies collectively is crucial, as moderate to strong connections exist among the technologies in improving built environment resilience.

To the best of the authors’ knowledge, this is one of the first papers that evaluate the effectiveness of Construction 4.0 technologies in improving built environment resilience. Industry professionals, researchers and policymakers can use the study findings to make well-informed decisions on selecting Construction 4.0 technologies that improve built environment resilience to climatic disasters.