A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming

Xu, Haowen; Windsor, Mary; Muste, Marian; Demir, İbrahim

doi:10.1016/j.jenvman.2019.109887

Cited by 54 publications

(35 citation statements)

References 18 publications

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“…A data inventory that embraces extensive information about flooding in terms of demographic, economic, urban, and agriculture perspectives will allow decision-makers to understand historical flood trends and analyze vulnerability and flood risk for the communities. The inventory can be enabled with artificial intelligence technologies to improve delivery and accessibility of the information [34,35] and used as a reference input for serious gaming platforms [36][37][38] to train decision-makers and the public.…”

Section: Flood Inventory Specificationmentioning

confidence: 99%

A comprehensive flood event specification and inventory: 1930–2020 Turkey case study

Haltaş

Yildirim

Oztas³

et al. 2021

International Journal of Disaster Risk Reduction

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Flooding is one of the most frequent natural disasters that have significant impact on communities in terms of loss of life, direct and indirect economic losses, and disruption of daily life. Decision makers often consult flood data inventories to make more informed decisions on the development of flood mitigation plans to protect flood prone communities. A comprehensive inventory that covers multiple aspects of a flood event is critical to identify vulnerable regions, historical trends, and mitigate possible flood impacts. This study proposes an integrated flood data specification to support multi-stakeholder use cases, community-based sustainable domain specific maintenance, and crowdsourced data collection and expansion. The specification is designed based on comprehensive review of existing global and national repositories, literature, needs and requirements of stakeholders. The specification is designed to cover environmental, economic, and demographic impact, hydraulic, hydrologic, and meteorological features, and detailed location information of a flood event. As a case study, a flood event inventory was compiled for Turkey between 1930-2020 using existing national and global data sources and digitized media archives. A total of 2,101 flood events with 64 data attributes have been collected over the last 90 years. An initial statistical analysis of the inventory is also presented for the seasonal and regional characteristics of flooding in Turkey.

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Section: Flood Inventory Specificationmentioning

confidence: 99%

A comprehensive flood event specification and inventory: 1930–2020 Turkey case study

Haltaş

Yildirim

Oztas³

et al. 2021

International Journal of Disaster Risk Reduction

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“…Significant improvements in the performance of web systems enabled optimized environmental data processing (Demir and Szczepanek, 2017;, geospatial analysis (Demir et al, 2018;Yildirim and Demir, 2019), and decision support systems with serious gaming (Xu et al, 2020;Carson et al, 2018;. Voluntary and crowdsourced citizen science approaches on the web enable community modeling efforts (Agliamzanov et al, 2019), environmental monitoring with smartphones, and social media analysis .…”

Section: 1-related Workmentioning

confidence: 99%

Web-based data analytics framework for well forecasting and groundwater quality

Sit

Langel

Thompson

et al. 2021

Science of The Total Environment

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“…Additional relevant advances are documented in Seo and Krajewski (2020), Ha et al (2020), Ghimire et al (2018), and Perez et al (2018). Many other technological developments have been demonstrated as concepts or prototypes Demir and Szczepanek 2017;Demir 2019, Yildirim andXu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A Regional Semi-Distributed Streamflow Model Using Deep Learning

Xiang¹,

Demir²,

Mantilla³

et al. 2021

Preprint

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Recent studies have shown that deep learning models in hydrological applications significantly improved streamflow predictions at multiple stations compared to traditional machine learning approaches. However, most studies lack generalization; i.e. researchers are training separate models for each location. The spatial and temporal generalization ability of deep learning models in hydrology that can be gained by training a single model for multiple stations is evaluated in this study. We developed a generalized model with a multi-site structure for hourly streamflow hindcasts on 125 USGS gauges. Considering watershed-scale features including drainage area, time of concentration, slope, and soil types, the proposed models have acceptable performance and slightly higher median NSE value than training individual models for each USGS station. Furthermore, we showed that the trained generalized model can be applied to any new gauge in the state of Iowa that was not used in the training set with acceptable accuracy. This study demonstrates the potential of deep learning studies in hydrology where more domain knowledge and physical features can support further generalization.

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A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming

Cited by 54 publications

References 18 publications

A comprehensive flood event specification and inventory: 1930–2020 Turkey case study

A comprehensive flood event specification and inventory: 1930–2020 Turkey case study

Web-based data analytics framework for well forecasting and groundwater quality

A Regional Semi-Distributed Streamflow Model Using Deep Learning

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