A survey of analytical methods for inclusion in a new energy-water nexus knowledge discovery framework

Allen, M. D.; Zaidi, Syed Mohammed Arshad; Chandola, Varun; Morton, April; Brelsford, Christa; McManamay, Ryan A.; Kc, Binita; Sanyal, Jibonananda; Stewart, Richard C.; Bhaduri, B. L.

doi:10.1080/20964471.2018.1524344

Cited by 8 publications

(7 citation statements)

References 163 publications

(158 reference statements)

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“…A public supply water service area, or service area, refers to the geographic boundary encompassing the population that receives water from one or more public water systems (Figure 1). Service areas are a suitable spatial resolution for estimating the CONUS water withdrawals as they align spatially with historically reported withdrawals and are geospatially linked to water users in the domestic, commercial, irrigation, and industrial sectors (Allen et al., 2018; Buchwald et al., 2023). A geospatial data set of service areas was created for the 18,807 public water service areas in the CONUS by linking site‐specific withdrawal sources to population, commercial, industrial, irrigation, and thermoelectric zones where public supply water is used (Buchwald et al., 2024).…”

Section: Datamentioning

confidence: 99%

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

Alzraiee,

Niswonger,

Luukkonen

et al. 2024

Water Resources Research

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Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.

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Section: Datamentioning

confidence: 99%

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

Alzraiee,

Niswonger,

Luukkonen

et al. 2024

Water Resources Research

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“…Although products that represent broad land components of energy sectors (e.g., developed land cover, mining) are available for the conterminous United States, current land use data products are limited in their ability to capture all (or at least the majority) of life cycle components of energy sector relevant to landscape analysis 15 . For instance, the national land use dataset (NLUD) is a 30 m product comprised of 79 anthropogenic land use classes and provides an extensive categorization of sectors for land use mapping; however, layers pertaining to energy life cycles and water sectors are limited 16 .…”

Section: Background and Summarymentioning

confidence: 99%

U.S. national water and energy land dataset for integrated multisector dynamics research

2022

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Understanding resource demands and tradeoffs among energy, water, and land socioeconomic sectors requires an explicit consideration of spatial scale. However, incorporation of land dynamics within the energy-water nexus has been limited due inconsistent spatial units of observation from disparate data sources. Herein we describe the development of a National Water and Energy Land Dataset (NWELD) for the conterminous United States. NWELD is a 30-m, 86-layer rasterized dataset depicting the land use of mappable components of the United States energy sector life cycles (and related water used for energy), specifically the extraction, development, production, storage, distribution, and operation of eight renewable and non-renewable technologies. Through geospatial processing and programming, the final products were assembled using four different methodologies, each depending upon the nature and availability of raw data sources. For validation, NWELD provided a relatively accurate portrayal of the spatial extent of energy life cycles yet displayed low measures of association with mainstream land cover and land use datasets, indicating the provision of new land use information for the energy-water nexus.

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“…Random forests are extensions of decision tree analysis that start with classification trees-types of decision trees that can be grown together as a "forest" in a computational system. They provide highly accurate classification and characterization of complex predictor variable interactions while maintaining flexible analytical technique selection (Allen et al, 2018). Random forests also provide the capability to deal with the issue of overfitting and multicollinearity as compared to the traditional linear regression models (Konapala and Mishra, 2020).…”

Section: Advancement In the Use Of Machine Learning Techniques For Drought Predictionmentioning

confidence: 99%

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

Allen‐Dumas

Kurte

et al. 2021

Front. Water

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Due to the complex interactions of human activity and the hydrological cycle, achieving urban water security requires comprehensive planning processes that address urban water hazards using a holistic approach. However, the effective implementation of such an approach requires the collection and curation of large amounts of disparate data, and reliable methods for modeling processes that may be co-evolutionary yet traditionally represented in non-integrable ways. In recent decades, many hydrological studies have utilized advanced machine learning and information technologies to approximate and predict physical processes, yet none have synthesized these methods into a comprehensive urban water security plan. In this paper, we review ways in which advanced machine learning techniques have been applied to specific aspects of the hydrological cycle and discuss their potential applications for addressing challenges in mitigating multiple water hazards over urban areas. We also describe a vision that integrates these machine learning applications into a comprehensive watershed-to-community planning workflow for smart-cities management of urban water resources.

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A survey of analytical methods for inclusion in a new energy-water nexus knowledge discovery framework

Cited by 8 publications

References 163 publications

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

U.S. national water and energy land dataset for integrated multisector dynamics research

Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

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