Twenty-first century urban water management: the imperative for holistic and cross-disciplinary approach

Younos, Tamim M.; Lee, Juneseok; Parece, Tammy E.

doi:10.1007/s13412-018-0524-3

Cited by 11 publications

(1 citation statement)

References 21 publications

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“…Some example questions a research can apply machine learning to include & Where are the most effective places to install rain gardens to maximize stormwater capture and natural treatment? (Younos et al 2019) A researcher can approach this question quite systematically. Using the rational method, where the peak discharge of stormwater is equal to the product of rainfall intensity, surface area and a runoff coefficient, areas in a community that prone to flooding can be quickly identified.…”

Section: Introductionmentioning

confidence: 99%

Decision support: using machine learning through MATLAB to analyze environmental data

Nadler

2019

J Environ Stud Sci

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Machine learning is not a tool that is available for use by computer scientists, but one that can and should be used by all researchers in this technological era. Gone are the days of humans solely relying on older techniques for decision support. The age of information we live in is filled with countless pieces of data and we need to use the correct tools to help make sense of it all. Using MATLAB and its machine learning tools is an excellent resource for environmental scientists to conduct deep-dives into their data. We use this software title to demonstrate some of its capabilities to enhance our research projects. Regression learning examines the capability of developing the best linear regression model based upon the selected independent and dependent variables. Clustering analysis displays how data can be grouped by similar characteristics and how distant they are from one another. Classification analysis can predict future outcomes depending upon historical input data, a crucial tool in developing models for impending environmental events. It is suggested that environmental scientists who have not incorporated machine learning into their research to begin to add it to their data analyses.

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Section: Introductionmentioning

confidence: 99%

Decision support: using machine learning through MATLAB to analyze environmental data

Nadler

2019

J Environ Stud Sci

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Drinking Water Infrastructure

Lee¹,

Güngör‐Demirci²,

Keck³

2019

Encyclopedia of Water

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This article provides an overview of our nation's drinking water infrastructure and discusses a range of critical and emerging concerns for the water sector. After an overview of the major and minor systems responsible for drinking water service, critical emerging concerns based on the authors' experience are then discussed relative to their hydraulic, water quality, and physical integrity linkages and implications. In addition to their direct impact on public health, these aspects also have significant socioeconomic implications. We then go on to consider other drinking water industry perspectives such as bottled water, rainwater harvesting, the water–energy nexus, decentralized water systems, and future directions for water systems management. It is the authors' hope that readers will develop a better understanding of the various issues and challenges that lie ahead for the drinking water sector, while also benefiting from our recommendations regarding future industry directions.

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