{
    "abstract": "In this research, our objective was to utilize different machine learning techniques, such as XGBoost, Extra Trees, CatBoost, and Multiple Linear Regression (MLR), to model the heating values of municipal solid waste. The input parameters considered for the constructed models included the weight of the dry sample (kg) and the content of carbon (C), hydrogen (H), oxygen (O), nitrogen (N), sulfur (S), and ash in kg. The Extra Trees model, fine-tuned for hyperparameters, demonstrated outstanding performance, achieving R",
    "authors": [
        {
            "affiliation": "Department of Environmental Health Engineering, Ferdows Faculty of Medical Sciences, Birjand University of Medical Sciences, Birjand, Iran.",
            "firstname": "Mansour",
            "initials": "M",
            "lastname": "Baziar"
        },
        {
            "affiliation": "Department of Environmental Health Engineering, School of Public Health, Khoy University of Medical Sciences, Khoy, Iran.",
            "firstname": "Mahmood",
            "initials": "M",
            "lastname": "Yousefi"
        },
        {
            "affiliation": "School of Life and Environmental Science, Deakin University, Geelong, Australia.",
            "firstname": "Vahide",
            "initials": "V",
            "lastname": "Oskoei"
        },
        {
            "affiliation": "Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran.",
            "firstname": "Ahmad",
            "initials": "A",
            "lastname": "Makhdoomi"
        },
        {
            "affiliation": "Department of Environmental Sciences and Policy, Central European University, Vienna, Austria.",
            "firstname": "Reza",
            "initials": "R",
            "lastname": "Abdollahzadeh"
        },
        {
            "affiliation": "Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran. aliakbardehghan@gmail.com.\nSocial Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. aliakbardehghan@gmail.com.",
            "firstname": "Aliakbar",
            "initials": "A",
            "lastname": "Dehghan"
        }
    ],
    "conclusions": null,
    "copyrights": "\u00a9 2025. The Author(s).",
    "doi": "10.1038/s41598-025-99432-8\n10.3390/ijerph16061060\n10.1016/j.watres.2024.121378\n10.3390/su13179963\n10.1016/j.enconman.2016.11.012\n10.1016/j.jclepro.2020.120495\n10.3390/pr11072092\n10.1016/j.ref.2018.09.005\n10.1016/j.rser.2011.05.003\n10.1016/j.fuel.2020.118122\n10.1016/j.fuel.2013.08.023\n10.1016/j.jclepro.2020.125575\n10.1016/j.wasman.2015.03.039\n10.1177/0734242X20941085\n10.15376/biores.16.2.Ram\n10.1016/j.energy.2023.127273\n10.3390/cleantechnol4040075\n10.1016/j.jenvman.2022.117174\n10.1016/j.jclepro.2022.135522\n10.1177/0734242X211033716\n10.1016/j.rser.2021.111977\n10.1016/j.jclepro.2023.138606\n10.1016/j.energy.2020.119279\n10.1016/j.energy.2019.116077\n10.1016/j.csite.2022.101823\n10.1016/j.chemosphere.2021.132135\n10.1007/s10994-006-6226-1\n10.1016/j.jhydrol.2020.125130\n10.1016/j.jece.2020.104599\n10.1016/j.jhydrol.2021.126382\n10.1007/s11709-024-1085-z\n10.1007/s40515-024-00433-3\n10.1016/j.biortech.2020.122926\n10.1007/s12145-024-01336-0\n10.1016/j.ecoinf.2024.102500\n10.1038/s41598-024-70939-w\n10.1007/s11709-024-1085-z\n10.1016/j.fuel.2016.04.112\n10.1038/s41598-023-43496-x\n10.1016/j.cam.2019.03.009\n10.1007/s00521-018-3870-x",
    "journal": "Scientific reports",
    "keywords": [
        "Extra trees",
        "Heating values",
        "Machine learning",
        "Municipal solid waste",
        "Prediction"
    ],
    "methods": null,
    "publication_date": "2025-04-27",
    "pubmed_id": "40287500\n30909625\n38430758\n25882791\n36586367\n34492416\n39198596\n37752284",
    "results": null,
    "title": "Machine learning-based prediction of heating values in municipal solid waste.",
    "xml": "<Element 'PubmedArticle' at 0x788504417ab0>"
}