An approach to designing sustainable urban infrastructure

Derrible, Sybil

doi:10.1557/mre.2018.14

Cited by 35 publications

(30 citation statements)

References 53 publications

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“…The Beijing-Tianjin-Hebei region is adjacent to China's most water-short river basin. The region suffers serious water scarcity problems [61][62][63].…”

Section: Cast Study: Beijing-tianjin-hebei Region In China 41 Study R...mentioning

confidence: 99%

Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Shuang

Zhao

2021

Water

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Predicting water demand helps decision-makers allocate regional water resources efficiently, thereby preventing water waste and shortage. The aim of this study is to predict water demand in the Beijing–Tianjin–Hebei region of North China. The explanatory variables associated with economy, community, water use, and resource availability were identified. Eleven statistical and machine learning models were built, which used data covering the 2004–2019 period. Interpolation and extrapolation scenarios were conducted to find the most suitable predictive model. The results suggest that the gradient boosting decision tree (GBDT) model demonstrates the best prediction performance in the two scenarios. The model was further tested for three other regions in China, and its robustness was validated. The water demand in 2020–2021 was provided. The results show that the identified explanatory variables were effective in water demand prediction. The machine learning models outperformed the statistical models, with the ensemble models being superior to the single predictor models. The best predictive model can also be applied to other regions to help forecast water demand to ensure sustainable water resource management.

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“…The Beijing-Tianjin-Hebei region is adjacent to China's most water-short river basin. The region suffers serious water scarcity problems [61][62][63].…”

Section: Cast Study: Beijing-tianjin-hebei Region In China 41 Study R...mentioning

confidence: 99%

Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Shuang

Zhao

2021

Water

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“…This is significantly lower than in the UK -6.6% of GDP, in Australia and New Zealand -6.9% of GDP, in Canada -8.1% of GDP [2]. Arguably, infrastructure management remains a barrier to achieving sustainable development goals [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Regional distribution of public-private partnership projects: case of Russia

2021

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In Russia, regional differentiation poses a threat to the development sustainability. The infrastructural gap between regions is a fundamental aspect of this differentiation. To explore possible solutions to this problem, we discuss the proactive attitude towards implementation of public-private partnership projects as an option for bridging the infrastructure gap. That is why we explore distribution dynamics and structure in the context of private partnership projects in Russia in 2007-2020. Based on findings, we concluded the following: first, as for the public-private partnership projects completed, their distribution across federal districts in Russia, as well as across economy sectors is uneven. Second, distribution of investments in implementation of public-private partnership projects by federal districts corresponds to quantitative distribution of projects. Third, distribution of investments in implementation of public-private partnership projects across sectors coincides with distribution of the projects completed. So, we might conclude that in some regions, public-private partnership projects are more capital-intensive and large-scale than in others. This might lead to increased regional differentiation. Overall implementation dynamics of public-private partnership projects in Russia suggests that their capacity is still in low demand.

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“…Building upon previous works, the main contribution of this article is to gain a fundamental understanding of the interrelationships between electricity, gas, and water consumption patterns. In the literature, an important knowledge gap exists to better understand how interconnected and interdependent the demand for and supply of urban infrastructure systems (Derrible, 2017;2018). This article further leverages advances in Machine Learning (ML) to uncover meaningful patterns between the consumption of electricity, gas, and water in buildings.…”

Section: Introductionmentioning

confidence: 99%

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Movahedi¹,

Derrible²

2020

Preprint

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As cities keep growing worldwide, so does the demand for key resources such as energy (electricity and gas) and water that residents consume. Meeting the demand for these resources can be challenging and requires an understanding of their consumptions patterns. In this work, we apply XGBoost (Extreme Gradient Boosting) to predict and analyze water and energy consumption in large-scale buildings in New York City. For this, the New York City’s local law 84 extensive dataset was merged with the Primary Land Use Tax Lot Output (PLUTO) dataset as well as with other socio-economic databases. Specifically, we developed three models: electricity, gas, and water consumption. Seven major lessons were learnt in terms of interrelationships between electricity, gas, and water consumption. In particular, water and gas consumption are highly interrelated with one another (often because gas is used for water heating). Furthermore, electricity consumption is affected by building type, and electricity and water consumption are particularly interrelated in nonresidential buildings. Overall, the knowledge gained from the models and from the SHAP analysis can help planners, engineers, and policymakers develop more effective strategies and help them manage the demand for energy and water in large-scale buildings.

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An approach to designing sustainable urban infrastructure

Abstract: ABSTRACT

Cited by 35 publications

References 53 publications

Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China

Regional distribution of public-private partnership projects: case of Russia

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

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