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  • 1.
    Khan, Zarrar
    et al.
    Pacific Northwest Natl Lab PNNL, Joint Global Change Res Inst JGCRI, College Pk, MD 20740 USA..
    Abraham, Edo
    Delft Univ Technol, Dept Water Resources Management, Delft, Netherlands..
    Aggarwal, Srijan
    Univ Alaska Fairbanks, Coll Engn & Mines, Fairbanks, AK USA..
    Khan, Manal Ahmad
    Natl Geog Partners, Washington, DC USA..
    Arguello, Ricardo
    Unidad Planificac Rural Agr UPRA, Bogota, Colombia..
    Babbar-Sebens, Meghna
    Oregon State Univ, Sch Civil & Construct Engn, Coll Engn, Corvallis, OR 97331 USA..
    Bereslawski, Julia Lacal
    Banco Interamer Desarrollo, Buenos Aires, DF, Argentina..
    Bielicki, Jeffrey M.
    Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH 43210 USA.;Ohio State Univ, John Glenn Coll Publ Affairs, Columbus, OH 43210 USA..
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Carrazzone, Maria Eugenia Silva
    FAO, Rome, Italy..
    Emerging Themes and Future Directions of Multi-Sector Nexus Research and Implementation2022In: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 10, article id 918085Article in journal (Refereed)
    Abstract [en]

    Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the "nexus". There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice.

  • 2.
    Saif-ul-Allah, M. W.
    et al.
    Process and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
    Qyyum, M. A.
    Department of Petroleum and Chemical Engineering, Sultan Qaboos University, Muscat, Oman.
    Ul-Haq, N.
    Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
    Salman, Chaudhary Awais
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Ahmed, F.
    Process and Energy Systems Engineering Center-PRESTIGE, Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.
    Gated Recurrent Unit Coupled with Projection to Model Plane Imputation for the PM2.5 Prediction for Guangzhou City, China2022In: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 9, article id 816616Article in journal (Refereed)
    Abstract [en]

    Air pollution is generating serious health issues as well as threats to our natural ecosystem. Accurate prediction of PM2.5 can help taking preventive measures for reducing air pollution. The periodic pattern of PM2.5 can be modeled with recurrent neural networks to predict air quality. To the best of the author’s knowledge, very limited work has been conducted on the coupling of missing value imputation methods with gated recurrent unit (GRU) for the prediction of PM2.5 concentration of Guangzhou City, China. This paper proposes the combination of project to model plane (PMP) with GRU for the superior prediction performance of PM2.5 concentration of Guangzhou City, China. Initially, outperforming the missing value imputation method PMP is proposed for air quality data under consideration by making a comparison study on various methods such as KDR, TSR, IA, NIPALS, DA, and PMP. Secondly, it presents GRU in combination with PMP to show its superiority on other machine learning techniques such as LSSVM and two other RNN variants, LSTM and Bi-LSTM. For this study, data for Guangzhou City were collected from China’s governmental air quality website. Data contained daily values of PM2.5, PM10, O3, SOx, NOx, and CO. This study has employed RMSE, MAPE, and MEDAE as model prediction performance criteria. Comparison of prediction performance criteria on the test data showed GRU in combination with PMP has outperformed the LSSVM and other RNN variants LSTM and Bi-LSTM for Guangzhou City, China. In comparison with prediction performance of LSSVM, GRU improved the prediction performance on test data by 40.9% RMSE, 48.5% MAPE, and 50.4% MEDAE. 

  • 3.
    Vörösmarty, C. J.
    et al.
    Advanced Science Research Center, City University of New York, New York, NY, United States.
    Campana, Pietro Elia
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Jewitt, G.
    IHE Delft Institute for Water Education Delft, Delft, Netherlands; Water Resources Research, University of KwaZulu-Natal Durban, Durban, South Africa.
    Lawford, R.
    Department of Computer, Mathematical & Natural Sciences (Retired), Morgan State University, Baltimore, MD, United States.
    Wuebbles, D.
    Department of Atmospheric Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
    Editorial: Food-energy-water systems: achieving climate resilience and sustainable development in the 21st century2023In: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 11, article id 1334892Article in journal (Refereed)
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