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May Haggag

Bio: May Haggag is an academic researcher from McMaster University. The author has contributed to research in topics: Resilience (network) & Climate change. The author has an hindex of 2, co-authored 3 publications receiving 7 citations.

Papers
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Journal ArticleDOI
15 Aug 2020
TL;DR: In this paper, the authors describe how to maintain their basic functions, cities have to be resilient and possess the ability to adapt to unforeseen events that take continue to take place worldwide.
Abstract: Given the unforeseen events that take continue to place worldwide, cities are experiencing rapid transformations. To maintain their basic functions, cities have to be resilient– possess the ability...

16 citations

Journal ArticleDOI
TL;DR: This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.
Abstract: The frequency of Climate-induced Disasters (CID) has tripled in the last three decades, driving the World Economic Forum to identify them as the most likely and most impactful risks worldwide. With more than 70% of the world population expected to be living in cities by 2050, ensuring the resilience of urban infrastructure systems under CID is crucial. The present work employs data analytics and machine learning techniques to develop a performance prediction framework for infrastructure systems under CID. The framework encompasses four stages related to: extracting meaningful information about the impact of CID on infrastructure systems and identifying the latter's performance; investigating the relationship between different CID attributes and previously identified system performance; employing data imputation using unsupervised machine learning techniques; and developing and testing a supervised machine learning model based on the different influencing CID attributes. To demonstrate its application, the developed framework is applied to disaster data compiled by the National Weather Services between 1996 and 2019 in the state of New York. The analysis results showed that: i) power systems in New York are the most vulnerable infrastructure to CID, and particularly to wind-related hazards; ii) power system performance level depends on hazard-system interactions rather than solely hazard characteristics; and iii) a 4-predictors random forest-based model can effectively predict power system performance with an accuracy of 89%. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.

9 citations

Journal ArticleDOI
TL;DR: In this paper, a deep learning model was developed for spatial-temporal disaster occurrence prediction, based on historical disaster data, global climate models, and climate change metrics, in an attempt to enhance urban resilience and mitigate CID risks on cities worldwide.
Abstract: The increased severity and frequency of Climate-Induced Disasters (CID) including those attributed to hydrological, meteorological, and climatological effects have been testing the resilience of cities worldwide. The World Economic Forum highlighted—in its 2020 Global Risk Report—that from 2018 to 2020, three of the top five risks with respect to likelihood and impact are climate related with extreme weather events being the highest ranked risk in terms of likelihood. To alleviate the adverse impacts of CID on cities, this paper aims at predicting the occurrence of CID by linking different climate change indices to historical disaster records. In this respect, a deep learning model was developed for spatial–temporal disaster occurrence prediction. To demonstrate its application, flood disaster data from the Canadian Disaster Database was linked to climate change indices data in Ontario in order to train, test and validate the developed model. The results of the demonstration application showed that the model was able to predict flood disasters with an accuracy of around 96%. In addition to its association with precipitation indices, the study results affirm that flood disasters are closely linked to temperature-related features including the daily temperature gradient, and the number of days with minimum temperature below zero. This work introduces a new perspective in CID prediction, based on historical disaster data, global climate models, and climate change metrics, in an attempt to enhance urban resilience and mitigate CID risks on cities worldwide.

9 citations


Cited by
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Journal ArticleDOI
TL;DR: Climate change and resilience are identified as key hot topics from authors’ keyword analysis, highlighting current research frontiers and laying out the directions for future research thrust in this critically important emerging research field.

28 citations

Journal ArticleDOI
TL;DR: A data-driven community flood resilience categorization framework that can be utilized for the development of realistic disaster management strategies and proactive risk mitigation measures to better protect urban centers from future catastrophic flood events is developed.
Abstract: Coupled with climate change, the expansive developments of urban areas are causing a significant increase in flood-related disasters worldwide. However, most flood risk analysis and categorization efforts have been focused on the hydrologic features of flood hazards (e.g., inundation depth, extent, and duration), rarely considering the resulting long-term losses and recovery time (i.e., the community's flood resilience). This paper aims at developing a data-driven community flood resilience categorization framework that can be utilized for the development of realistic disaster management strategies and proactive risk mitigation measures to better protect urban centers from future catastrophic flood events. This approach considers key resilience goals such as the robustness of the exposed community and its recovery rapidity. Such categorization that calls on the two resilience means, namely resourcefulness and redundancy, can empower decision makers to learn from past events and guide future resilience strategies. To demonstrate the applicability of the developed framework, a data-driven framework was applied on historical mainland flood disaster records collected by the US National Weather Services between 1996 and 2019. Descriptive analysis was conducted to identify the features of this dataset as well as the interdependence between the different variables considered. To further demonstrate the utilization of the developed data-driven framework, a spatial analysis was conducted to quantify community flood resilience across different counties within the affected states. Beyond the work presented in this paper, the developed framework lays the foundation to adopt data driven approaches for disasters prediction to guide proactive risk mitigation measures and develop community resilience management insights.

14 citations

Journal ArticleDOI
TL;DR: In this paper , the authors examine practices for incorporating resilience by transportation agencies and suggest the need for innovative and practical methods, processes, and information for resilience integration in transportation planning and project development.
Abstract: The objective of this paper is to examine practices for incorporating resilience by transportation agencies. This paper presents findings from state-wide interviews and survey of personnel in transportation organizations throughout Texas. This study is focused on resilience planning and practices (not emergency management and evacuation planning). The research examines the state of resilience incorporation in transportation project planning and development. Our analysis reveals the gap between resilience research and engineering practice and highlights the need for innovative and practical methods, processes, and information for resilience integration. The findings suggest that while there are a few available frameworks for incorporating resilience, the implementation of these tools varies significantly in different districts and organizations. Elements needed to facilitate consideration of resilience in transportation planning and project development include proper policy and procedures, adequate funding and financing, knowledgeable staff, within- and cross-organization coordination, and proper tools, metrics, and data. This research helps facilitate prioritization and strategic improvement of efforts for resilience integration in transportation planning and project development.

10 citations

Journal ArticleDOI
TL;DR: This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.
Abstract: The frequency of Climate-induced Disasters (CID) has tripled in the last three decades, driving the World Economic Forum to identify them as the most likely and most impactful risks worldwide. With more than 70% of the world population expected to be living in cities by 2050, ensuring the resilience of urban infrastructure systems under CID is crucial. The present work employs data analytics and machine learning techniques to develop a performance prediction framework for infrastructure systems under CID. The framework encompasses four stages related to: extracting meaningful information about the impact of CID on infrastructure systems and identifying the latter's performance; investigating the relationship between different CID attributes and previously identified system performance; employing data imputation using unsupervised machine learning techniques; and developing and testing a supervised machine learning model based on the different influencing CID attributes. To demonstrate its application, the developed framework is applied to disaster data compiled by the National Weather Services between 1996 and 2019 in the state of New York. The analysis results showed that: i) power systems in New York are the most vulnerable infrastructure to CID, and particularly to wind-related hazards; ii) power system performance level depends on hazard-system interactions rather than solely hazard characteristics; and iii) a 4-predictors random forest-based model can effectively predict power system performance with an accuracy of 89%. This work is expected to aid stakeholders in developing spatio-temporal preparedness plans under CID, which can facilitate mitigating the adverse impacts of CID on infrastructure systems and improve their resilience.

9 citations

Journal ArticleDOI
TL;DR: A number of recent consensus reports suggest four priorities for indicators that portray the impacts of climate change, climate-related extreme events, and other driving forces on infrastructure.
Abstract: Built infrastructures are increasingly disrupted by climate-related extreme events. Being able to monitor what climate change implies for US infrastructures is of considerable importance to all levels of decision-makers. A capacity to develop cross-cutting, widely applicable indicators for more than a dozen different kinds of infrastructure, however, is severely limited at present. The development of such indicators must be considered an ongoing activity that will require expansion and refinement. A number of recent consensus reports suggest four priorities for indicators that portray the impacts of climate change, climate-related extreme events, and other driving forces on infrastructure. These are changes in the reliability of infrastructure services and the implications for costs; changes in the resilience of infrastructures to climate and other stresses; impacts due to the interdependencies of infrastructures; and ongoing adaptation in infrastructures.

8 citations