GIS-based sinkhole susceptibility mapping using the best worst method

Maleki, Mohammad; Salman, Mohammad Shukri; Vayghan, Saeideh Sahebi; Szabó, Szilárd

doi:10.1007/s41324-023-00520-6

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…Sinkholes are hollows in the ground surface formed by the dissolution of limestone or geological features, considered closed cavities drained due to subsoil dissolution in karst rocks [ 1 ]. This land subsidence and collapse represents a major hazard that substantially impacts economic and human losses [ 2 ], as well as in nearby infrastructures [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration

Orbea,

Cruz Ulloa,

Del Cerro

et al. 2023

Sensors

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The presence of sinkholes has been widely studied due to their potential risk to infrastructure and to the lives of inhabitants and rescuers in urban disaster areas, which is generally addressed in geotechnics and geophysics. In recent years, robotics has gained importance for the inspection and assessment of areas of potential risk for sinkhole formation, as well as for environmental exploration and post-disaster assistance. From the mobile robotics approach, this paper proposes RUDE-AL (Roped UGV DEployment ALgorithm), a methodology for deploying a Mobile Cable-Driven Parallel Robot (MCDPR) composed of four mobile robots and a cable-driven parallel robot (CDPR) for sinkhole exploration tasks and assistance to potential trapped victims. The deployment of the fleet is organized with node-edge formation during the mission’s first stage, positioning itself around the area of interest and acting as anchors for the subsequent release of the cable robot. One of the relevant issues considered in this work is the selection of target points for mobile robots (anchors) considering the constraints of a roped fleet, avoiding the collision of the cables with positive obstacles through a fitting function that maximizes the area covered of the zone to explore and minimizes the cost of the route distance performed by the fleet using genetic algorithms, generating feasible target routes for each mobile robot with a configurable balance between the parameters of the fitness function. The main results show a robust method whose adjustment function is affected by the number of positive obstacles near the area of interest and the shape characteristics of the sinkhole.

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Section: Introductionmentioning

confidence: 99%

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration

Orbea,

Cruz Ulloa,

Del Cerro

et al. 2023

Sensors

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Mobile CDPR System for Robotic Sinkhole Exploration

Orbea,

Ulloa,

Del Cerro

et al. 2024

2024 10th International Conference on Automation, Robotics and Applications (ICARA)

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Comparison of the Analytic Network Process and the Best–Worst Method in Ranking Urban Resilience and Regeneration Prioritization by Applying Geographic Information Systems

Hosseini,

Shakeryari,

Nazari Nejad

et al. 2024

Land

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Urbanization without planning causes concerns about biodiversity loss, congestion, housing, and ecosystem sustainability in developing countries. Therefore, resilience and regeneration following urbanization are critical to city planning and sustainable development. Integrating multi-criteria decision-making methods (MCDM) with geographic information systems (GIS) can be a promising method for analyzing city resilience and regeneration. This study aims to use two MCDMs, the Analytic Network Process (ANP) and the Best–Worst Method (BWM), to evaluate the resilience of metropolitan neighborhoods in Tehran. Fourteen criteria were selected to represent the city’s resilience, and the weights of two models were evaluated for their spatial patterns using GIS. The results showed that the building age was the most important criterion in both methods, while the per capita green space was the least important criterion. The weights of the most important criterion, the building age, for the ANP and BWM, were 19.56 and 18.98, respectively, while the weights of the least important criterion, the per capita green space, were 2.197 and 1.655, respectively. Therefore, the MCDM with GIS provides an approach for assessing city resilience and regeneration priority.

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GIS-based sinkhole susceptibility mapping using the best worst method

Cited by 3 publications

References 38 publications

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration

Mobile CDPR System for Robotic Sinkhole Exploration

Comparison of the Analytic Network Process and the Best–Worst Method in Ranking Urban Resilience and Regeneration Prioritization by Applying Geographic Information Systems

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