Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data

Zhi, Guozheng; Liao, Zhenliang; Tian, Wenchong; Wu, Jiang

doi:10.1016/j.jenvman.2020.110521

Cited by 41 publications

(24 citation statements)

References 49 publications

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“…However, this should not be a problem, as such analyses require high-quality building models that may only be obtained via terrestrial lidar scanning, photogrammetry, or manual modeling [42]. Models produced by the proposed method may be used for a variety of purposes, such as large-scale modeling and simulation of flooding, where (in comparison to commonly used LOD1 datasets), their more detailed representation of original building shapes may allow for more accurate calculation of the percentage in which a building will be covered in water [43], or for an optimized preview of separately available high-resolution datasets [44]. Moreover, the proposed algorithm may also be used for the purpose of generating lower levels of detail for high-quality models obtained using other techniques [45].…”

Section: Discussionmentioning

confidence: 99%

A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

Kulawiak

2022

Remote Sensing

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The recent popularization of airborne lidar scanners has provided a steady source of point cloud datasets containing the altitudes of bare earth surface and vegetation features as well as man-made structures. In contrast to terrestrial lidar, which produces dense point clouds of small areas, airborne laser sensors usually deliver sparse datasets that cover large municipalities. The latter are very useful in constructing digital representations of cities; however, reconstructing 3D building shapes from a sparse point cloud is a time-consuming process because automatic shape reconstruction methods work best with dense point clouds and usually cannot be applied for this purpose. Moreover, existing methods dedicated to reconstructing simplified 3D buildings from sparse point clouds are optimized for detecting simple building shapes, and they exhibit problems when dealing with more complex structures such as towers, spires, and large ornamental features, which are commonly found e.g., in buildings from the renaissance era. In the above context, this paper proposes a novel method of reconstructing 3D building shapes from sparse point clouds. The proposed algorithm has been optimized to work with incomplete point cloud data in order to provide a cost-effective way of generating representative 3D city models. The algorithm has been tested on lidar point clouds representing buildings in the city of Gdansk, Poland.

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Section: Discussionmentioning

confidence: 99%

A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

Kulawiak

2022

Remote Sensing

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“…In disaster loss assessment and forecasting, scholars have mainly focused on earthquakes (Jena et al, 2020;Kim et al, 2020;Pulinets et al, 2021), tropical cyclones (Qi and Du, 2018;Sawant et al, 2019;Giffard-Roisin et al, 2020;Zeng et al, 2021), and floods (Zhi et al, 2020;Soltani et al, 2021), but few studies have been conducted on storm surge. With the background and impact of global climate change (Hao et al, 2021), the problem of marine disasters (Fang et al, 2017;Yan et al, 2020) is becoming increasingly obvious.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

2022

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Modeling the direct economic losses of storm surge disasters can assess the disaster situation in a timely manner and improve the efficiency of post-disaster management in practice, which is acknowledged as one of the most significant issues in clean production. However, improving the forecasting accuracy of direct economic losses caused by storm surge disasters remains challenging, which is also a major concern in the field of disaster risk management. In particular, most of the previous studies have mainly focused on individual models, which ignored the significance of reduction and optimization. Therefore, a novel direct economic loss forecasting system for storm surge disasters is proposed in this study, which includes reduction, forecasting, and evaluation modules. In this system, a forecasting module based on an improved machine learning technique is proposed, which improves the generalization ability and robustness of the system. In addition, the key attributes and samples are selected by the proposed reduction module to further improve the forecasting performance from the two innovative perspectives. Moreover, an evaluation module is incorporated to comprehensively evaluate the superiority of the developed forecasting system. Data on the storm surge disasters from three typical provinces are utilized to conduct a case study, and the performance of the proposed forecasting system is analyzed and compared with eight comparison models. The experimental results show that the mean absolute percentage error (MAPE) predicted by the Extreme Learning Machine (ELM) model was 16.5293%, and the MAPE predicted by the proposed system was 1.0313%. Overall, the results show that the performance of the proposed forecasting system is superior compared to other models, and it is suitable for the forecasting of direct economic losses resulting from storm surge disasters.

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“…Facing the knowledge gap among urban flood risk management, innovative use of computer-based visualisation and virtual reality (VR) technology has been shown to encourage greater engagement amongst diverse participants. A combined simulation-visualisation platform can become an important shared learning tool and there are good prospects for developing an interactive model through the use of computer-based visualisation and virtual reality technology (Wang et al, 2019;Zhi et al, 2020;Yang et al, 2021). The innovation will be helpful for practitioners to communicate and perceive an extreme flood event.…”

Section: Inter-model and Interdisciplinary Approachesmentioning

confidence: 99%

“…electricity substations, bridges, and drainage system), and indirect consequences, such as loss of productivity and business opportunities, can occur (Barredo et al, 2012). Numerous studies have reported that urban surface water flooding has caused tremendous socio-economic loss, which is expected to increase in severity and frequency in the future with urbanisation, economic development, and more frequent extreme weather (CRED, 2015;IPCC, 2014;Bernet et al, 2017;Barredo, 2009;Zhou et al, 2013;Moncoulon et al, 2016). IPCC (2014) indicated that climate change will cause extreme precipitation events that are more intense and frequent in many regions, thus leading to greater flood risks.…”

Section: Introductionmentioning

confidence: 99%

Urban surface water flood modelling – a comprehensive review of current models and future challenges

Guo

Guan

2021

Hydrol. Earth Syst. Sci.

116

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Abstract. Urbanisation is an irreversible trend as a result of social and economic development. Urban areas, with high concentration of population, key infrastructure, and businesses, are extremely vulnerable to flooding and may suffer severe socio-economic losses due to climate change. Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas. These tools have been rapidly developing in recent decades. In this study, we present a comprehensive review of the advanced urban flood models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. The study explores the advantages and limitations of existing model types, highlights the most recent advances, and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.

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Urban flood risk assessment and analysis with a 3D visualization method coupling the PP-PSO algorithm and building data

Cited by 41 publications

References 49 publications

A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

A Cost-Effective Method for Reconstructing City-Building 3D Models from Sparse Lidar Point Clouds

Modeling of Direct Economic Losses of Storm Surge Disasters Based on a Novel Hybrid Forecasting System

Urban surface water flood modelling – a comprehensive review of current models and future challenges

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