High resolution schemes for hyperbolic conservation laws

The COVID-19 pandemic: Impacts on cities and major lessons for urban planning, design, and management

BigData andmachine learning (ML) technologies have the potential to impactmany facets of environment andwatermanagement (EWM). BigData are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and socialmedia have contributed to the proliferation of BigData inmany EWMfields, such asweather forecasting, disastermanagement, smart water and energymanagement systems, and remote sensing. BigData brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics.ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn fromdata.MLmay help unlock the power of BigData if properly integratedwith data analytics. Recent breakthroughs inAI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features fromdata, with better predictive performance and less human intervention. Collectively BigData andML techniques have shown great potential for data-driven decisionmaking, scientific discovery, and process optimization. These technological advancesmay greatly benefit EWM, especially because (1)many EWMapplications (e.g. early floodwarning) require the capability to extract useful information from a large amount of data in autonomousmanner and in real time, (2)EWMresearches have become highlymultidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge aboutmany EWMprocesses is still incomplete, but whichmay now be complemented through data-driven discovery. A large number of applications onBigData andML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in BigData andML, (3) provide a systematic review of current applications, andfinally (4) discussmajor issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM,with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWMresearch.

https://iopscience.iop.org/article/10.1088/1748-9326/ab1b7d/pdf

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

The analysis of data from social media and social networking sites may be instrumental in achieving a better understanding of human-environment interactions and in shaping future conservation and environmental management. In this study, we systematically map the application of social media data in environmental research. The quantitative review of 169 studies reveals that most studies focus on the analysis of people’s behavior and perceptions of the environment, followed by environmental monitoring and applications in environmental planning and governance. The literature testifies to a very rapid growth in the field, with Twitter (52 studies) and Flickr (34 studies) being most frequently used as data sources. A growing number of studies combine data from multiple sites and jointly investigates multiple types of media. A broader, more qualitative review of the insights provided by the investigated studies suggests that while social media data offer unprecedented opportunities in terms of data volume, scale of analysis, and real-time monitoring, researchers are only starting to cope with the challenges of data’s heterogeneity and noise levels, potential biases, ethics of data acquisition and use, and uncertainty about future data availability. Critical areas for the development of the field include integration of different types of information in data mashups, development of quality assurance procedures and ethical codes, improved integration with existing methods, and assurance of long-term, free and easy-to-access provision of public social media data for future environmental researchers.

Passive crowdsourcing of social media in environmental research: A systematic map

The utility of social media for both collecting and disseminating information during natural disasters is increasingly recognised. The rapid nature of urban flooding from intense rainfall means accurate surveying of peak depths and flood extents is rarely achievable, hindering the validation of urban flood models. This paper presents a real-time modelling framework to identify areas likely to have flooded using data obtained only through social media. Graphics processing unit (GPU) accelerated hydrodynamic modelling is used to simulate flooding in a 48-km2 area of Newcastle upon Tyne, with results automatically compared against flooding identified through social media, allowing inundation to be inferred elsewhere in the city with increased detail and accuracy. Data from Twitter during two 2012 flood events are used to test the framework, with the inundation results indicative of good agreement against crowd-sourced and anecdotal data, even though the sample of successfully geocoded Tweets was relatively small.

/pdf/assessing-the-utility-of-social-media-as-a-data-source-for-1k5vw63i30.pdf

Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework

Towards a generalised GPU/CPU shallow-flow modelling tool

A new High-Performance Integrated hydrodynamic Modelling System (Hi-PIMS) is tested for urban flood simulation. The software solves the two-dimensional shallow water equations using a first-order accurate Godunov-type shock-capturing scheme incorporated with the Harten, Lax and van Leer approximate Riemann solver with the contact wave restored (HLLC) for flux evaluation. The benefits of modern graphics processing units are explored to accelerate large-scale high-resolution simulations. In order to test its performance, the tool is applied to predict flood inundation due to rainfall and a point source surface flow in Glasgow, Scotland, and a hypothetical inundation event at different spatial resolutions in Thamesmead, England, caused by embankment failure. Numerical experiments demonstrate potential benefits for high-resolution modelling of urban flood inundation, and a much-improved level of performance without compromising result quality.

/pdf/a-high-performance-integrated-hydrodynamic-modelling-system-181zmm9xm0.pdf

A High-Performance Integrated Hydrodynamic Modelling System for Urban Flood Simulations

This commentary describes the rapid development of a COVID-19 data dashboard utilising existing Urban Observatory Internet of Things (IoT) data and analytics infrastructure. Existing data capture s...

https://journals.sagepub.com/doi/pdf/10.1177/2043820620934211

Smart cities and a data-driven response to COVID-19

We present a new hydrodynamic modelling framework capable of fully exploiting modern graphics and central processing units (GPUs and CPUs) from any of the mainstream vendors, to be used in the design and assessment of sustainable drainage systems. A finite-volume Godunov-type scheme is combined with the HLLC Riemann solver to create a robust numerical model which correctly addresses wetting and drying and transient flow conditions, and is suitable for application to a wide range of flood simulations. The software is tested with a three day flood event in Carlisle during 2005, at resolutions from 25 m to 2 m. Run-times are significantly reduced without compromising numerical accuracy. Excellent agreement is found between the simulation results and a comprehensive post-event survey. Changes in sensitivity to Manning's n are examined at different resolutions, with changes to the floodplain found to have little influence at 2 m resolution.

/pdf/towards-a-hydrodynamic-modelling-framework-appropriate-for-3sor9n6kqv.pdf

Luke Smith

Papers

Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework

Towards a generalised GPU/CPU shallow-flow modelling tool

A High-Performance Integrated Hydrodynamic Modelling System for Urban Flood Simulations

Smart cities and a data-driven response to COVID-19

Towards a hydrodynamic modelling framework appropriate for applications in urban flood assessment and mitigation using heterogeneous computing