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 6 publications

(4 citation statements)

References 163 publications

(158 reference statements)

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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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Section: Background and Summarymentioning

confidence: 99%

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

2022

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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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“…This explosion in the quantity and variety of data relevant to the WEN reflects a broader shift in global environmental governance toward the use of Smart Earth technologies, Big Data analytics, and machine learning (Bakker & Ritts, 2018; Kitchin, 2014; UNEP, 2020). Within this literature, visualizations are often positioned as the most effective way to synthesize and communicate vast amounts of data to heterogeneous audiences engaged in resource use planning and management (Allen et al, 2018; O'Neill et al, 2017; UNEP, 2020).…”

Section: Wen Visualizations and The Question Of Hydrosocialitymentioning

confidence: 99%

Visualizing water‐energy nexus landscapes

et al. 2021

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Over the past decade, the water-energy nexus (WEN) has emerged as a prominent framework with which to analyze and visualize interconnections between energy production, freshwater resources, and the hydrological cycle. The WEN is a fundamentally geographic concept embedded in landscapes. WEN analyses often include landscape visualizations, yet these are rarely conceptually rigorous; consequently, the visual-representational dimensions of WEN analyses remain relatively weak. Our paper addresses this gap through a meta-review of 503 WEN visualizations sourced from 336 scholarly articles. Based on this analysis, we argue that WEN visualizations often depict complex landscapes as technical systems, while eliding broader considerations of the multiscalar, spatiotemporal, and hydrosocial dimensions of water and energy. In response to these limitations, we offer an alternative approach to visualizing hydrosocial landscapes that draws upon parallel work in geography and cognate disciplines.In the concluding section of the paper, we formulate a set of interdisciplinary recommendations to guide the production of more theoretically-informed nexus visualizations grounded in the concepts of spatiality, temporality, and hydrosociality.

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

Cited by 6 publications

References 163 publications

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

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

Visualizing water‐energy nexus landscapes

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