{"title":"Machine learning insights in predicting heavy metals interaction with biochar","authors":"","doi":"10.1007/s42773-024-00304-7","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The use of machine learning (ML) in the field of predicting heavy metals interaction with biochar is a promising field of research, mainly because of the growing understanding of how removal efficiency is affected by characteristic variables, reaction conditions and biochar properties. The practical application in biochar still faces large challenges, such as difficulties in data collection, inadequate algorithm development, and insufficient information. However, the quantity, quality, and representation of data have a large impact on the accuracy, efficiency, and generalizability of machine learning tasks. From this perspective, the present data descriptors, the efficiency of machine learning-aided property and performance prediction, the interpretation of underlying mechanisms and complicated relationships, and some potential ways to augment the data are discussed regarding the interactions of heavy metals with biochar. Finally, future perspectives and challenges are discussed, and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective.</p> <span> <h3>Graphical Abstract</h3> <p> <span> <span> <img alt=\"\" src=\"https://static-content.springer.com/image/MediaObjects/42773_2024_304_Figa_HTML.png\"/> </span> </span></p> </span>","PeriodicalId":8789,"journal":{"name":"Biochar","volume":"8 1","pages":""},"PeriodicalIF":13.1000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biochar","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s42773-024-00304-7","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
The use of machine learning (ML) in the field of predicting heavy metals interaction with biochar is a promising field of research, mainly because of the growing understanding of how removal efficiency is affected by characteristic variables, reaction conditions and biochar properties. The practical application in biochar still faces large challenges, such as difficulties in data collection, inadequate algorithm development, and insufficient information. However, the quantity, quality, and representation of data have a large impact on the accuracy, efficiency, and generalizability of machine learning tasks. From this perspective, the present data descriptors, the efficiency of machine learning-aided property and performance prediction, the interpretation of underlying mechanisms and complicated relationships, and some potential ways to augment the data are discussed regarding the interactions of heavy metals with biochar. Finally, future perspectives and challenges are discussed, and an enhanced model performance is proposed to reinforce the feasibility of a particular perspective.
期刊介绍：
Biochar stands as a distinguished academic journal delving into multidisciplinary subjects such as agronomy, environmental science, and materials science. Its pages showcase innovative articles spanning the preparation and processing of biochar, exploring its diverse applications, including but not limited to bioenergy production, biochar-based materials for environmental use, soil enhancement, climate change mitigation, contaminated-environment remediation, water purification, new analytical techniques, life cycle assessment, and crucially, rural and regional development. Biochar publishes various article types, including reviews, original research, rapid reports, commentaries, and perspectives, with the overarching goal of reporting significant research achievements, critical reviews fostering a deeper mechanistic understanding of the science, and facilitating academic exchange to drive scientific and technological development.
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