Improving urban flood resilience via GDELT GKG analyses in China's Sponge Cities.

Urban floods are the most severe disaster in most Chinese cities due to rapid urbanisation and climate challenges. Recently, media data analytics has become prominent in enhancing urban flood resilience. In this study, news media data from the GKG of the GDELT project was innovatively used to examine the pattern of news media responses towards urban flooding in China's Sponge City Programme (SCP) pilot cities. We find that public sentiments toward urban flood events have been more positive in SCP pilot cities from 2015 to 2021. News media responses towards urban floods exhibit strong seasonality, which is significantly connected with rainfall patterns. Most of the media articles were posted during the urban flood event. Finally, we suggest the opportunities and challenges in applying GKG data analytics and new technologies for urban flood resilience. The results can provide beneficial references to urban flood management strategies in China's Sponge Cities for associated policymakers and stakeholders.

An overview of flood-induced transport disruptions on urban streets and roads in Chinese megacities: Lessons and future agendas.

Urban road transport disruptions caused by urban floods have become severe in the Chinese megacities due to climate change and urbanisation. Urban road planning, design, and land drainage systems are insufficiently coping with intense rainstorms, especially in the wet season. This is reflected in more research findings on urban flood impacts and road transport disruption over the past decade. Here we provide a critical overview of current research on urban road inundation, road traffic delays, and accessibility losses under flood conditions, and illustrate up-to-date practices with the relevant governmental institutions. Our review implies that urban flood management in road design is still at an embryonic stage in the Chinese megacities. Hence, we review the lessons and experiences of urban flood impacts on roads in the global context. We argue that it is essential to enhance better co-production practices on emergency responses and recovery measures between authorities, which is vital to improving flood resilience in uncertain climates.

Supply chain redesign implications to information disruption impact.

Over the years, supply chain reconfiguration decisions have been solely based on operational risk. Simplification strategies, such as horizontal mergers, and networking strategies, such as risk pooling, are conflicting paradigms that have been shown to improve financial performance of supply partners. The implication of this to disruption risk is not fully known, especially as it concerns information security breach (ISB). Analysts have rated ISB as a huge disruption risk, costing businesses millions of dollars. Using a credible and well-established agent-based simulation approach and statistical analysis, we examine the impact of ISB on the simplification and risk pooling strategies respectively under three different order replenishment systems. The effect of reconfiguring the supply chain is first examined in a non-security breach scenario and then in a breached scenario. We find that reconfiguration has no benefit to a supply chain using a parameter based replenishment policy (option I), in both breach and non-breach situations, but leads to significant advantage when batch ordering model (option II) or a combined batch-and-parameter based ordering policy (option III) is used. We also established that batch ordering system favours the risk pooling strategy whereas a combined batch-and-parameter ordering system favours the simplification counterpart especially when the simplification is at the wholesaler tier. This study has significant implications for supply chain design as well as information security priorities. This is one of the first papers to look at how ISB impacts supply chain configuration and the role of ordering decision context.

Recent Development in Big Data Analytics for Business Operations and Risk Management.

"Big data" is an emerging topic and has attracted the attention of many researchers and practitioners in industrial systems engineering and cybernetics. Big data analytics would definitely lead to valuable knowledge for many organizations. Business operations and risk management can be a beneficiary as there are many data collection channels in the related industrial systems (e.g., wireless sensor networks, Internet-based systems, etc.). Big data research, however, is still in its infancy. Its focus is rather unclear and related studies are not well amalgamated. This paper aims to present the challenges and opportunities of big data analytics in this unique application domain. Technological development and advances for industrial-based business systems, reliability and security of industrial systems, and their operational risk management are examined. Important areas for future research are also discussed and revealed.

Cascading Delay Risk of Airline Workforce Deployments with Crew Pairing and Schedule Optimization.

This article concerns the assignment of buffer time between two connected flights and the number of reserve crews in crew pairing to mitigate flight disruption due to flight arrival delay. Insufficient crew members for a flight will lead to flight disruptions such as delays or cancellations. In reality, most of these disruption cases are due to arrival delays of the previous flights. To tackle this problem, many research studies have examined the assignment method based on the historical flight arrival delay data of the concerned flights. However, flight arrival delays can be triggered by numerous factors. Accordingly, this article proposes a new forecasting approach using a cascade neural network, which considers a massive amount of historical flight arrival and departure data. The approach also incorporates learning ability so that unknown relationships behind the data can be revealed. Based on the expected flight arrival delay, the buffer time can be determined and a new dynamic reserve crew strategy can then be used to determine the required number of reserve crews. Numerical experiments are carried out based on one year of flight data obtained from 112 airports around the world. The results demonstrate that by predicting the flight departure delay as the input for the prediction of the flight arrival delay, the prediction accuracy can be increased. Moreover, by using the new dynamic reserve crew strategy, the total crew cost can be reduced. This significantly benefits airlines in flight schedule stability and cost saving in the current big data era.