A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Xu, Haowen; Demir, İbrahim; Koylu, Caglar; Muste, Marian

doi:10.1016/j.scitotenv.2019.07.157

Cited by 51 publications

(36 citation statements)

References 38 publications

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“…Random forests Xu et al (2019a) demonstrated decent performance of random forests for forecasting sedimentation risks at culverts by validating the results with field inspection.…”

Section: Annmentioning

confidence: 87%

“…Altunkaynak, 2009;Yadav et al, 2019b Unsupervised techniques These methods are self-organizing, and their results are often validated using domain models and knowledge. For example, Xu et al (2019a) used the concept of geological landform regions to verify the clustering results of sedimentation potential from a self-organizing map. Ahmed et al, 2018;Xu et al, 2019a Urban infrastructure (section 2.4.1)…”

Section: Annmentioning

confidence: 99%

“…For example, Xu et al (2019a) used the concept of geological landform regions to verify the clustering results of sedimentation potential from a self-organizing map. Ahmed et al, 2018;Xu et al, 2019a Urban infrastructure (section 2.4.1)…”

Section: Annmentioning

confidence: 99%

“…Random forests, SOM Xu et al (2019a) showed that ML application can be used not only for multi-risk assessment and hazard prediction but also for exploring the complex and interconnected processes behind multiple hazards.…”

Section: Chen Et Al 2019mentioning

confidence: 99%

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Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

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“…Random forests Xu et al (2019a) demonstrated decent performance of random forests for forecasting sedimentation risks at culverts by validating the results with field inspection.…”

Section: Annmentioning

confidence: 87%

Section: Annmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Section: Chen Et Al 2019mentioning

confidence: 99%

See 2 more Smart Citations

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

Self Cite

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“…Transportation networks are one of the essential infrastructures for the functioning of a society and economy (Guze, 2014). These networks may experience adverse impacts, including floods, earthquakes, and terrorist attacks affecting their performance, efficiency, and accessibility (Dehghani et al, 2014;Xu et al, 2019). Decisions about the placement of shelters, temporary services, and potential evacuation routes must be made within the context of current and predicted flood extents and scopes.…”

Section: Introductionmentioning

confidence: 99%

Wayfinding and Accessibility Analysis for Critical Amenities in Iowa During Flood Events

Alabbad¹,

Mount²,

Campbell³

et al. 2020

Preprint

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Transportation infrastructure plays an essential role in flood response and recovery. However, flooding may disturb road functionality and generate direct and indirect adverse impacts, including the loss of access to essential services. This paper presents a comprehensive analysis of flood impacts on road network topology and accessibility to amenities for major communities in the State of Iowa using graph-theoretic methods, including single-source shortest path analyses. We assessed the disruption of transportation networks on the accessibility to critical amenities (e.g., hospitals) under 100 and 500-year flood scenarios. Our analysis methodology leads toward the development of an integrated real-time decision support system that will allow decision-makers to explore “what if” flood scenarios to identify vulnerable areas and population in their authority. Due to locations and effects on road topology under flood events, the results show differential impacts in access to critical services.

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Next Generation Web Technologies for Hydrological Applications on Client-side Systems

Demir¹

2020

Preprint

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A web-based geovisual analytics platform for identifying potential contributors to culvert sedimentation

Cited by 51 publications

References 38 publications

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Wayfinding and Accessibility Analysis for Critical Amenities in Iowa During Flood Events

Next Generation Web Technologies for Hydrological Applications on Client-side Systems

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