Data Analytics for Environmental Science and Engineering Research

Gupta, Suraj; Aga, Diana S.; Pruden, Amy; Zhang, Liqing; Vikesland, Peter J.

doi:10.1021/acs.est.1c01026

Cited by 62 publications

(32 citation statements)

References 107 publications

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“…The self-detection of the model uses the mean squared error (MSE), average absolute percentage (MAPE), root-mean-square error (RSME), mean absolute error (MAE), median absolute error (MedAE), and mean squared logarithmic error (MSLE) , as follows: where n is the number of decision tree models, actual( t ) is the actual UEC of a WWTP, and predicted( t ) is the predicted UEC of a WWTP.…”

Section: Data and Methodsmentioning

confidence: 99%

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Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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Wastewater treatment plants (WWTPs) can account for up to 1% of a country’s energy consumption. Meanwhile, WWTPs have high energy-saving potential. To achieve this, it is necessary to establish appropriate energy consumption models for WWTPs. Several recent models have been developed using logarithmic, exponential, or linear functions. However, the behavior of WWTPs is non-linear and difficult to fit with simple functions, particularly for non-numerical variables. Thus, traditional modeling methods cannot effectively describe the relationship between water and energy in WWTPs. Therefore, a machine learning method was adopted in this study to investigate the energy consumption in WWTPs; a novel energy consumption model with a non-numerical variable (discharge standard) for WWTPs was developed using the random forest algorithm. The model can also predict the energy consumption of WWTPs after upgrading discharge standards. We found that the unit electricity consumption of WWTPs exhibited an average increase of 17% after the effluent discharge standard was increased from class I B to class I A (as per China’s classification). The correlation coefficient of the model was 0.702. Thus, the developed model can provide a better understanding of energy efficiency in WWTPs.

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Section: Data and Methodsmentioning

confidence: 99%

“…The self-detection of the model uses the mean squared error (MSE), average absolute percentage (MAPE), root-meansquare error (RSME), mean absolute error (MAE), median absolute error (MedAE), and mean squared logarithmic error (MSLE) 40,41 as follows:…”

mentioning

confidence: 99%

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

2021

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“…The rapid growth in data volume, variety, and complexity are driving the need for a robust culture of data sharing and open science, which are steadily becoming more mainstream. , This shift will allow researchers to better leverage advances in data science and accelerate the pace of research. By integrating and analyzing data sets across disciplines, new information can be revealed and new solutions identified that were not envisioned when focusing on a narrow research topic. − To harness this potential, more emphasis is needed on understanding how to maximize the reuse of existing scientific data while also adapting how new research is designed and conducted to ensure data is collected with sharing in mind.…”

Section: Why Fair?mentioning

confidence: 99%

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

Heacock

Lopez²,

Amolegbe

et al. 2022

Environ. Sci. Technol.

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Environmental health sciences (EHS) span many diverse disciplines. Within the EHS community, the National Institute of Environmental Health Sciences Superfund Research Program (SRP) funds multidisciplinary research aimed to address pressing and complex issues on how people are exposed to hazardous substances and their related health consequences with the goal of identifying strategies to reduce exposures and protect human health. While disentangling the interrelationships that contribute to environmental exposures and their effects on human health over the course of life remains difficult, advances in data science and data sharing offer a path forward to explore data across disciplines to reveal new insights. Multidisciplinary SRP-funded teams are well-positioned to examine how to best integrate EHS data across diverse research domains to address multifaceted environmental health problems. As such, SRP supported collaborative research projects designed to foster and enhance the interoperability and reuse of diverse and complex data streams. This perspective synthesizes those experiences as a landscape view of the challenges identified while working to increase the FAIR-ness (Findable, Accessible, Interoperable, and Reusable) of EHS data and opportunities to address them.

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“…After the network topology and network state equations are established, the preliminary network hydraulic calculation can be carried out. After the analysis, we can get the information on the network conditions, including nodal water pressure, pipe section flow, pipe section head loss, pipe network water quality condition, water plant discharge pressure, and water plant discharge flow, etc., and we can use the modeling software to draw the corresponding parameter images for observation [24].…”

Section: Multivariate Statistical Model Design For Water Leakage Of U...mentioning

confidence: 99%

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

Chen¹,

Yang

2022

Mathematical Problems in Engineering

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In this paper, model construction of random matrix theory is carried out to analyze water leakage of urban water supply pipeline network, and a multivariate statistical model of water leakage of urban water supply pipeline network based on random matrix theory is proposed. This paper presents a three-level DMA, short-term water demand prediction method based on water use patterns, aiming to solve the problem of inaccurate water demand prediction caused by climate and social factors. Correlation analysis and factor analysis analyze the main factors affecting daily water demand changes. The extracted information of the main influencing factors and historical monitoring data are used to establish the Elman water demand prediction model. At the same time, the daily water consumption pattern was extracted by correlation analysis, matched with the water consumption pattern of the predicted day, and combined with the expected value of water demand for momentary water demand allocation, which improved the prediction accuracy of short-term water demand. This paper is based on the precise leakage location based on the real-time hydraulic model. The real-time hydraulic model under leakage conditions is established, GA and NSGA2 are designed, and the nondominated ranking algorithm with elite strategy is used to achieve accurate leakage location using hydraulic model calibration search. This paper uses a random matrix algorithm to establish the leakage estimation model. A laboratory case pipe network is built to simulate the water supply conditions in residential areas, and the operating data of the laboratory case pipe network are used to confirm that the random matrix leakage estimation model can estimate the leakage. The applicability of the random matrix leakage estimation model and the wavelet transform algorithm for leakage calculation are compared in an engineering example. The example confirms that the leakage amount calculated by the random matrix model has a less relative error and more similar trend than the wavelet transform algorithm.

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Data Analytics for Environmental Science and Engineering Research

Cited by 62 publications

References 107 publications

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Novel Machine Learning-Based Energy Consumption Model of Wastewater Treatment Plants

Enhancing Data Integration, Interoperability, and Reuse to Address Complex and Emerging Environmental Health Problems

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

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