[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5191":3,"related-5191":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":57,"slug":60,"view_count":15,"doi":61,"paper":62,"created_at":79},5191,"Artificial intelligence in immovable cultural heritage research: a bibliometric and knowledge mapping review","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs43238-026-00324-1","Abstract In recent years, the application of artificial intelligence (AI) in immovable cultural heritage research has expanded rapidly, supporting new approaches to heritage documentation, condition assessment, conservation, management, and public communication. In this study, immovable cultural heritage primarily refers to historic buildings, archaeological sites, monuments, cultural landscapes, and other heritage places with fixed spatial locations. Movable cultural objects, museum collections, and intangible cultural heritage are not systematically examined unless the relevant studies directly address the documentation, monitoring, restoration, conservation, or management of immovable heritage. Despite the growing volume of research, existing studies have largely focused on individual technical applications, while comprehensive bibliometric analyses and knowledge-structure mapping from a global perspective remain limited. To address this gap, this study retrieved 382 English-language publications from the Web of Science Core Collection covering the period from 1 January 2015 to 1 October 2025. Using bibliometric methods and analytical tools including VOSviewer, CiteSpace, and ArcGIS, the study examines publication trends, geographical distribution, disciplinary categories, major authors, institutional affiliations, collaboration networks, thematic evolution, and emerging research topics. The results show that publication output increased substantially after 2020. China, Italy, and Spain were among the most productive countries and occupied important positions in the international collaboration network. Major research themes included digital heritage, structural health monitoring, virtual reconstruction, deep learning, remote sensing, and sustainability-related applications. These findings indicate that AI-related research on immovable cultural heritage is strongly represented in engineering, conservation science, computer science, remote sensing, and related technical fields. However, this pattern should not be interpreted as evidence of a general disciplinary shift across cultural heritage studies as a whole. Rather, it reflects the technological orientation of the literature captured by the present search strategy, which was limited to English-language publications indexed in the Web of Science Core Collection. This study maps the development and knowledge structure of AI applications in immovable cultural heritage research and provides quantitative evidence for identifying major research areas, collaboration patterns, methodological tendencies, and future research needs.","摘要 近年来，人工智能（AI）在不可移动文物保护研究中的应用迅速扩展，为遗产记录、状况评估、保护、管理和公众传播提供了新的方法路径。本研究中的不可移动文物主要指历史建筑、考古遗址、纪念物、文化景观及其他具有固定空间位置的遗产场所。可移动文物、博物馆藏品和非物质文化遗产不在系统考察范围内，除非相关研究直接涉及不可移动遗产的记录、监测、修复、保护或管理。尽管研究数量不断增长，现有研究大多聚焦于单项技术应用，而从全球视角开展的综合文献计量分析和知识结构图谱仍较为有限。为弥补这一空白，本研究从Web of Science核心合集检索了2015年1月1日至2025年10月1日期间的382篇英文文献，运用文献计量方法及VOSviewer、CiteSpace和ArcGIS等分析工具，考察了发文趋势、地理分布、学科类别、主要作者、机构隶属、合作网络、主题演化及新兴研究议题。结果表明，2020年后发文量显著增长。中国、意大利和西班牙是发文量最高的国家之一，并在国际合作网络中占据重要位置。主要研究主题包括数字遗产、结构健康监测、虚拟重建、深度学习、遥感及可持续性相关应用。这些发现表明，AI相关的不可移动文物研究在工程学、保护科学、计算机科学、遥感及相关技术领域具有较强的代表性。然而，这一格局不应被解释为整个文化遗产研究领域普遍发生学科转向的证据，而是反映了本研究所采用的检索策略所捕获文献的技术导向，该策略仅限于Web of Science核心合集收录的英文文献。本研究梳理了AI在不可移动文物保护研究中的应用发展与知识结构，并提供了定量证据",null,"Built Heritage","2026-10-15T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文研究不可移动文化遗产领域的AI文献计量，与三农、农业信息化、智慧农业、数字乡村无关，相关性门槛未通过。",[19],{"name":10,"url":6},[21,22,23],"人工智能","文献计量","文化遗产",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":56},[31,41,48],{"code":32,"score":33,"supported":34,"title":35,"abstract":37,"tags":40},"I2",18,true,[36],"artificial intelligence",[38,39,36],"深度学习","deep learning",[21],{"code":42,"score":43,"supported":34,"title":44,"abstract":46,"tags":47},"I7",15,[45],"bibliometric",[22,45],[22],{"code":49,"score":50,"supported":34,"title":51,"abstract":52,"tags":55},"I3",6,[],[53,54],"遥感","remote sensing",[],"农业相关性需要复核",[58,59],"不可移动文物 人工智能","人工智能 文化遗产 文献计量","不可移动文物人工智能-5191","10.1186\u002Fs43238-026-00324-1",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":15,"oa_url":70,"card":71,"direction":77,"ingested_from":78},"W7222247878",[65,68],{"name":66,"orcid":67},"Yating Wen","https:\u002F\u002Forcid.org\u002F0009-0003-0289-3199",{"name":69,"orcid":9},"Marzudi Md Yunus","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs43238-026-00324-1.pdf",{"tldr":72,"method":73,"finding":74,"direction":75,"opportunity":76},"用文献计量与知识图谱方法梳理2015-2025年AI在不可移动文化遗产研究中的应用格局。","Web of Science核心合集382篇英文文献，用VOSviewer、Ci","2020年后发文量激增，中意西三国领先，主题集中于数字遗产、结构健康监测、虚拟重建、深度学习与遥感。","其他","可借鉴其遥感与深度学习主题，探索AI在农业文化遗产、传统村落等不可移动农业遗产监测保护中的空白。","数字乡村与农业信息化","openalex","2026-10-17T23:30:41.024676Z",{"total":50,"page":81,"page_size":50,"items":82},1,[83,128,193,288,359,402],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":9,"source_name":89,"source_url":86,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":91,"sources":93,"tags":95,"primary_topic":9,"secondary_topics":98,"classification":99,"search_phrases":110,"slug":113,"view_count":81,"doi":114,"paper":115,"created_at":127},4496,"Artificial Intelligence and Cultural Heritage: A Review of Opportunities, Risks, and Ethical Challenges","https:\u002F\u002Fdoi.org\u002F10.61424\u002Fijcgrs.v3i1.1060","Artificial intelligence (AI) is increasingly transforming the documentation, preservation, interpretation, and public engagement of cultural heritage. This review examines the opportunities, risks, and ethical challenges associated with the application of AI in cultural heritage management and research. Drawing on interdisciplinary literature spanning heritage studies, computer science, digital humanities, museum studies, and information ethics, the review synthesizes evidence on the use of machine learning, computer vision, natural language processing, generative AI, and three-dimensional technologies in heritage contexts. The findings indicate that AI can enhance the digitization and classification of cultural collections, reconstruct damaged or lost heritage assets, support archaeological analysis, improve multilingual access to heritage resources, and create more interactive experiences for museums and heritage sites. AI also offers opportunities for predictive conservation through automated monitoring of environmental conditions, material deterioration, and structural changes. However, these benefits are accompanied by significant risks, including algorithmic bias, inaccurate reconstruction, cultural misrepresentation, intellectual-property disputes, data privacy concerns, technological dependency, and the marginalization of communities whose cultural knowledge is incorporated into AI systems without meaningful participation or consent. Generative AI presents additional challenges because synthetic representations may blur distinctions between historically documented evidence and machine-generated interpretations. The review therefore emphasizes the importance of human oversight, transparent documentation of AI-generated outputs, culturally sensitive data governance, community participation, provenance tracking, and equitable access to digital heritage technologies. Overall, AI should be understood not simply as a technological tool but as a socio-cultural intervention whose deployment requires collaboration among technologists, heritage professionals, researchers, governments, and source communities. Responsible and inclusive AI governance can enable technological innovation while protecting the authenticity, diversity, ownership, and cultural significance of heritage resources.","人工智能（AI）正日益改变文化遗产的记录、保存、阐释与公众参与方式。本文综述了AI在文化遗产管理与研究中应用所带来的机遇、风险与伦理挑战。通过整合涵盖遗产研究、计算机科学、数字人文、博物馆学与信息伦理学的跨学科文献，本文综合了机器学习、计算机视觉、自然语言处理、生成式AI及三维技术在遗产领域应用的证据。研究结果表明，AI能够促进文化藏品的数字化与分类，重建受损或遗失的遗产资产，支持考古分析，改善遗产资源的多语言获取，并为博物馆与遗产地创造更具互动性的体验。AI还通过自动监测环境条件、材料劣化与结构变化，为预测性保护提供了机遇。然而，这些益处伴随着显著风险，包括算法偏见、重建不准确、文化误现、知识产权争议、数据隐私问题、技术依赖，以及那些其文化知识在缺乏实质性参与或同意的情况下被纳入AI系统的社区所面临的边缘化。生成式AI带来了额外挑战，因为合成表征可能模糊历史文献证据与机器生成阐释之间的界限。因此，本文强调人工监督、AI生成产出的透明记录、文化敏感的数据治理、社区参与、来源追踪以及数字遗产技术公平获取的重要性。总体而言，AI不应仅被理解为一种技术工具，而应被视为一种社会文化干预，其部署需要技术人员、遗产专业人员、研究者、政府与来源社区之间的协作。负责任且包容的AI治理能够在保护遗产资源真实性、多样性、所有权与文化意义的同时，推动技术创新。","OpenAlex","2026-10-07T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":92},"主题为AI在文化遗产领域的综述，与三农、农业信息化、智慧农业无直接关联，不进入每日精选。",[94],{"name":89,"url":86},[21,23,96,97],"数字人文","伦理治理",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":100},{"candidates":101,"agricultural_evidence":14,"reason":56},[102],{"code":32,"score":33,"supported":34,"title":103,"abstract":104,"tags":109},[36],[105,106,107,36,108],"机器学习","计算机视觉","machine learning","computer vision",[21],[111,112],"人工智能 伦理治理 数字人文 文化遗产","人工智能 伦理治理","人工智能伦理治理数字人文文化遗产-4496","10.61424\u002Fijcgrs.v3i1.1060",{"doi":114,"openalex_id":116,"authors":117,"venue":9,"cited_by_count":15,"oa_url":120,"card":121,"direction":126,"ingested_from":78},"W7220858187",[118],{"name":119,"orcid":9},"Morgan Roberts","https:\u002F\u002Fbluemarkpublishers.com\u002Findex.php\u002FIJCGRS\u002Farticle\u002Fdownload\u002F1060\u002F916",{"tldr":122,"method":123,"finding":124,"direction":75,"opportunity":125},"综述AI在文化遗产管理中的机遇、风险与伦理挑战，强调负责任治理。","跨学科文献综述，涵盖机器学习、计算机视觉、生成式AI与3D技术。","AI可助力遗产数字化与预测性保护，但存在偏见、误重建与社区边缘化风险。","可探索文化遗产AI的社区参与式数据治理与生成内容溯源机制，平衡创新与真实性。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:11.121326Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":142,"tags":144,"primary_topic":149,"secondary_topics":155,"classification":156,"search_phrases":171,"slug":174,"view_count":15,"doi":175,"paper":176,"created_at":192},5493,"Mapping Research on the Implementation of Smart Village Policies in Indonesia: A Bibliometric Study for the Years 2017-2025","https:\u002F\u002Fdoi.org\u002F10.66254\u002Fjgssvol5issue2page333-347","This research aims to analyze the role of public sector policies in the development of Smart Villages in Indonesia, focusing on the utilization of information and communication technology (ICT) to improve the quality of life in rural communities. Although the Indonesian government has launched various programs such as the Digital Village Program and Smart City, the implementation of these policies still faces various challenges, including limited infrastructure, low digital literacy, and lack of inter-agency coordination. This research uses a bibliometric approach to map relevant research and policy trends, as well as to identify challenges and opportunities in the development of Smart Villages. The research findings indicate that despite progress, existing public sector policies have not yet been fully effective in addressing the digital divide between villages and cities. This research recommends the need for more inclusive and integrated policy improvements, as well as strengthening human resource capacities in villages to support the creation of sustainable and competitive Smart Villages.","本研究旨在分析公共部门政策在印度尼西亚智慧村（Smart Villages）发展中的作用，重点关注利用信息通信技术（ICT）提升农村社区生活质量。尽管印度尼西亚政府已推出数字村计划（Digital Village Program）和智慧城市（Smart City）等多项举措，但这些政策的实施仍面临诸多挑战，包括基础设施有限、数字素养偏低以及跨机构协调不足。本研究采用文献计量学方法，梳理相关研究与政策趋势，并识别智慧村发展中的挑战与机遇。研究结果表明，尽管取得了一定进展，现有公共部门政策在弥合城乡数字鸿沟方面尚未充分发挥效用。本研究建议有必要进行更具包容性和整合性的政策改进，同时加强农村人力资源能力建设，以支持创建可持续且具有竞争力的智慧村。","Journal of Government Science Studies\u002FJournal Of Government Science Studies","2026-10-20T00:00:00Z",71,{"impact":138,"substance":33,"depth":43,"authority":139,"freshness":140,"relevant":81,"comment":141},16,13,9,"该文献计量研究系统梳理了印尼智慧乡村政策实施进展与数字鸿沟挑战，对数字乡村国际比较有参考价值，但属区域性研究，影响层级有限。",[143],{"name":134,"url":131},[145,146,147,22,148],"数字乡村","印尼","数字鸿沟","智慧乡村",{"code":150,"slug":151,"name":152,"path":153,"parent_code":154},"I6","digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":157,"locked":14,"version":27,"method":28,"evidence":158},"classified",{"candidates":159,"agricultural_evidence":34},[160,164],{"code":42,"score":43,"supported":34,"title":161,"abstract":162,"tags":163},[45],[22,45],[22],{"code":150,"score":165,"supported":34,"title":166,"abstract":167,"tags":170},11,[],[147,168,169],"digital village","digital divide",[145,147],[172,173],"印尼 智慧乡村","印尼 数字村 数字鸿沟","印尼智慧乡村-5493","10.66254\u002Fjgssvol5issue2page333-347",{"doi":175,"openalex_id":177,"authors":178,"venue":134,"cited_by_count":15,"oa_url":131,"card":187,"direction":77,"ingested_from":78},"W7223067869",[179,181,184],{"name":180,"orcid":9},"Syaifulrijal Mahulauw",{"name":182,"orcid":183},"Wahab Tuanaya","https:\u002F\u002Forcid.org\u002F0009-0001-9236-8149",{"name":185,"orcid":186},"Marno Wance","https:\u002F\u002Forcid.org\u002F0000-0001-6663-7598",{"tldr":188,"method":189,"finding":190,"direction":77,"opportunity":191},"用文献计量法梳理2017-2025年印尼智慧村政策实施研究，识别挑战与机会。","文献计量分析，覆盖2017-2025年相关文献与政策趋势。","政策尚未有效弥合城乡数字鸿沟，需更包容整合的政策与人力建设。","可延伸至跨国比较或政策效果量化评估，聚焦数字鸿沟与跨部门协同机制。","2026-10-21T23:30:33.962760Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":207,"tags":209,"primary_topic":213,"secondary_topics":217,"classification":224,"search_phrases":260,"slug":263,"view_count":15,"doi":264,"paper":265,"created_at":287},5400,"Artificial intelligence in food safety: a bibliometric and empirical synthesis of emerging technologies and research frontiers","https:\u002F\u002Fdoi.org\u002F10.1108\u002Fbfj-06-2025-0819","Purpose This study aimed to identify recent research trends in the integration of artificial intelligence (AI) with food safety and security through a comprehensive bibliometric analysis. Design\u002Fmethodology\u002Fapproach A total of 2,782 documents published between 2021 and 2025 were retrieved from the Web of Science Core Collection. The dataset was refined by removing duplicates, filtering document types and excluding irrelevant records. VOSviewer was then used to conduct co-authorship, keyword co-occurrence, citation, co-citation and bibliographic coupling analyses. Findings Machine learning and deep learning emerged as core technologies associated with food security, contamination detection and quality prediction. Keyword clustering demonstrated interdisciplinary connections with agriculture, climate change and public health. Citation and co-citation analyses identified three major intellectual foundations: traditional machine learning, computer vision-based deep learning and time-series modeling. China, India and the United States were the most influential contributors in terms of research output and technological development. Bibliographic coupling and source co-citation analyses further indicated increasing convergence between food science and computational science. Originality\u002Fvalue This study provides a comprehensive overview of the intellectual structure, collaborative networks and thematic evolution of AI-driven food safety research. Unlike previous studies focusing on specific technologies or applications, it maps global research patterns and interdisciplinary linkages. The findings provide strategic insights for researchers, policymakers and industry stakeholders. Future studies should prioritize explainable AI integration into regulatory frameworks and the development of larger, more diverse datasets to improve model generalizability across food systems.","目的 本研究旨在通过全面的文献计量分析，识别人工智能（AI）与食品安全及保障融合领域的最新研究趋势。设计与方法 从Web of Science核心合集中检索了2021年至2025年间发表的2，782篇文献。通过去除重复记录、筛选文献类型并排除不相关记录对数据集进行精炼。随后使用VOSviewer进行合著、关键词共现、引文、共被引及文献耦合分析。研究发现 机器学习和深度学习成为与食品安全、污染检测和质量预测相关的核心技术。关键词聚类显示了与农业、气候变化和公共卫生的跨学科联系。引文和共被引分析识别出三大主要知识基础：传统机器学习、基于计算机视觉的深度学习和时间序列建模。中国、印度和美国在研究产出和技术发展方面是最具影响力的贡献者。文献耦合和来源共被引分析进一步表明食品科学与计算科学之间的融合日益加深。原创性\u002F价值 本研究全面概述了AI驱动的食品安全研究的知识结构、合作网络和主题演变。与以往聚焦特定技术或应用的研究不同，本研究绘制了全球研究模式和跨学科联系。研究结果为研究人员、政策制定者和行业利益相关者提供了战略性见解。未来研究应优先关注可解释AI融入监管框架以及开发更大、更多样化的数据集，以提高模型在食品系统中的泛化能力。","British Food Journal","2026-10-19T00:00:00Z",80,{"impact":33,"substance":203,"depth":33,"authority":204,"freshness":205,"relevant":81,"comment":206},22,14,8,"基于2782篇文献的AI食品安全计量分析，方法规范、结论有参考价值，但属综述性研究，产业落地影响有限。",[208],{"name":199,"url":196},[210,211,105,212,22],"智慧农业","农业人工智能","食品安全",{"code":32,"slug":214,"name":215,"path":216,"parent_code":154},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[218],{"code":219,"slug":220,"name":221,"path":222,"parent_code":223},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":157,"locked":14,"version":27,"method":28,"evidence":225},{"candidates":226,"agricultural_evidence":34},[227,232,237,241,248,255],{"code":32,"score":228,"supported":34,"title":229,"abstract":230,"tags":231},19,[36],[38,105,106,107,39,36,108],[211,105],{"code":219,"score":43,"supported":34,"title":233,"abstract":235,"tags":236},[234],"food safety",[212,234],[212],{"code":42,"score":43,"supported":34,"title":238,"abstract":239,"tags":240},[45],[22,45],[22],{"code":242,"score":50,"supported":34,"title":243,"abstract":244,"tags":247},"H7",[],[245,246],"气候变化","climate change",[],{"code":249,"score":250,"supported":34,"title":251,"abstract":252,"tags":254},"A8",3,[],[253],"food security",[],{"code":256,"score":81,"supported":14,"title":257,"abstract":258,"tags":259},"I1",[],[],[210],[261,262],"AI 食品安全 文献计量","机器学习 食品质量预测","AI食品安全文献计量-5400","10.1108\u002Fbfj-06-2025-0819",{"doi":264,"openalex_id":266,"authors":267,"venue":199,"cited_by_count":15,"oa_url":9,"card":281,"direction":285,"ingested_from":78},"W7222920742",[268,270,272,275,278],{"name":269,"orcid":9},"Harang Lee",{"name":271,"orcid":9},"지은서",{"name":273,"orcid":274},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":276,"orcid":277},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":279,"orcid":280},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":282,"method":283,"finding":284,"direction":285,"opportunity":286},"对2021-2025年2782篇文献做文献计量，梳理AI与食品安全交叉研究趋势。","Web of Science文献计量，VOSviewer共被引、关键词共现与耦合","机器学习与深度学习是核心，形成传统ML、计算机视觉、时序建模三大知识基础。","农业人工智能与决策模型","可解释AI嵌入监管框架、构建更大更多样数据集以提升跨食物系统泛化能力。","2026-10-20T23:30:31.567802Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":294,"source_url":291,"published_at":295,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":296,"score_detail":297,"sources":300,"tags":302,"primary_topic":9,"secondary_topics":305,"classification":306,"search_phrases":329,"slug":332,"view_count":333,"doi":334,"paper":335,"created_at":358},4881,"Mapping the knowledge structure and evolution of artificial intelligence for water reuse and sustainable water management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02389-4","Artificial intelligence (AI) has become a powerful instrument to solve complex problems related to sustainable water resource management because it allows creating advanced, data-driven modelling, prediction, and optimization of water systems. Although the studies in this sphere have increased quickly, the global scientific tendencies have not been thoroughly examined. This research paper presents a bibliometric review of the literature about AI applications in green water management that was published from 2015 to 2025. The Scopus database was searched with a structured search strategy on bibliographic data, and 3,280 peer-reviewed articles were analyzed. VOSviewer was used to perform bibliometric mapping and network visualization to investigate the trends in publications, most active journals, most impactful authors, contributions of institutions, country-level partnerships, and research clusters. The findings indicate that the publishing activity has grown tremendously in the last ten years, which suggests that there is a growing interest in AI-based solutions to water quality monitoring, hydrological prediction, optimization of wastewater treatment, and smart water management system. China, the United States and India became the most productive nations with the University of Chinese Academy of Sciences and Beijing normal university as some of the top contributing institutions. The analysis of the key words co-occurrence (dominant research themes) was based on machine learning, water-quality prediction, hydrological modelling, remote sensing, and decision-support systems. The co-citation analysis has shown the core studies in machine learning algorithms, including random forests, neural networks, and boosting algorithms, which underpin the present studies in this field. Overall, the results depict the growing role of AI technologies to enhance the effectiveness of water resources management and wastewater treatment. This research offers a clear insight into the intellectual framework and the future research perspective of AI-based environmentally sustainable water management which can be of great value to researchers, policymakers, and practitioners who would wish to come up with innovative and data-based water management solutions.","人工智能（AI）已成为解决可持续水资源管理相关复杂问题的有力工具，因为它能够构建先进的数据驱动模型，实现水系统的预测与优化。尽管该领域的研究迅速增加，但全球科学趋势尚未得到充分审视。本文对2015年至2025年间发表的关于人工智能在绿色水资源管理中应用的文献进行了文献计量学综述。采用结构化检索策略对Scopus数据库中的文献数据进行检索，共分析了3，280篇同行评审论文。使用VOSviewer进行文献计量制图和网络可视化，以探究发文趋势、最活跃期刊、最具影响力作者、机构贡献、国家层面合作及研究聚类。研究结果表明，过去十年间发文活动急剧增长，表明基于人工智能的水质监测、水文预测、废水处理优化和智能水管理系统解决方案日益受到关注。中国、美国和印度成为最高产国家，中国科学院和北京师范大学为贡献最多的机构之一。关键词共现分析（主导研究主题）基于机器学习、水质预测、水文建模、遥感和决策支持系统。共被引分析揭示了机器学习算法的核心研究，包括随机森林、神经网络和提升算法，这些构成了该领域当前研究的基础。总体而言，研究结果描绘了人工智能技术在提升水资源管理和废水处理效能方面日益增长的作用。本研究为基于人工智能的环境可持续水资源管理的知识框架和未来研究视角提供了清晰见解，对希望提出创新性和数据驱动型水资源管理方案的研究人员、政策制定者和实践者具有重要价值。","Discover Artificial Intelligence","2026-10-12T00:00:00Z",67,{"impact":298,"substance":33,"depth":138,"authority":139,"freshness":205,"relevant":81,"comment":299},12,"基于3280篇文献的AI水资源管理文献计量综述，方法规范、结论有参考价值，但属学术综述类，产业与政策落地性有限。",[301],{"name":294,"url":291},[211,53,22,303,304],"水资源管理","智慧水务",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":307},{"candidates":308,"agricultural_evidence":14,"reason":56},[309,313,321,325],{"code":32,"score":33,"supported":34,"title":310,"abstract":311,"tags":312},[36],[105,107,36],[211],{"code":314,"score":315,"supported":34,"title":316,"abstract":317,"tags":320},"H3",7,[],[318,319],"水资源","water resource",[303],{"code":49,"score":315,"supported":34,"title":322,"abstract":323,"tags":324},[],[53,54],[53],{"code":42,"score":315,"supported":34,"title":326,"abstract":327,"tags":328},[],[22,45],[22],[330,331],"AI 水资源管理 文献计量","人工智能 再生水 可持续水管理","AI水资源管理文献计量-4881",2,"10.1007\u002Fs44163-026-02389-4",{"doi":334,"openalex_id":336,"authors":337,"venue":294,"cited_by_count":15,"oa_url":352,"card":353,"direction":126,"ingested_from":78},"W7221506022",[338,341,344,346,348,350],{"name":339,"orcid":340},"Narendra Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5578-3255",{"name":342,"orcid":343},"Mangey Ram Nagar","https:\u002F\u002Forcid.org\u002F0000-0003-3635-3548",{"name":345,"orcid":9},"Isha Dharsandia",{"name":347,"orcid":9},"Ananya Tiwari",{"name":349,"orcid":9},"Santosh Kumar Mishra",{"name":351,"orcid":9},"Mohit Kumar","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44163-026-02389-4.pdf",{"tldr":354,"method":355,"finding":356,"direction":285,"opportunity":357},"对2015-2025年AI用于水回用与可持续水管理的3280篇文献做文献计量分析，梳理知识结构与演化","Scopus检索3280篇同行评议文献，用VOSviewer做共现与共被引网络可","发文量十年激增，中国、美国、印度领先；主题聚焦机器学习、水质预测、水文建模、遥感与决策支持。","可将AI水管理方法迁移至农业灌溉与面源污染场景，构建农业水-肥-碳协同的智能决策模型。","2026-10-14T23:30:10.246538Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":367,"sources":369,"tags":371,"primary_topic":9,"secondary_topics":374,"classification":375,"search_phrases":383,"slug":386,"view_count":15,"doi":387,"paper":388,"created_at":401},4817,"Use of artificial intelligence and other data-driven technologies for coastal management","https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835","Biogeochemical cycles occur naturally in coastal regions. Estuaries, wetlands, and tidal creeks assist these cycles. Since increased anthropogenic activities and naturally occurring events act as stressors, they impact physical, chemical, and biological processes in maritime spaces and hence these cycles. This makes existing ecosystems susceptible to trophic imbalance. Resultantly, researchers began monitoring and mapping coastal environments to understand how different stressors impact these biogeochemical processes using numerical and empirical models. With advances in computation infrastructure, use of data-driven techniques such as Artificial Intelligence, Machine Learning, and Deep Learning in different hydrological systems have become commonplace. This has invigorated the use of predictive and decision-making frameworks in coastal management, making habitat conservation more scientific. Such frameworks balance ecological health and human activities through hard and soft engineering, nature-based solutions, and managed retreat. Though substantial literature on the use of Artificial Intelligence and Machine Learning in the coastal environment exists, they focus on independent domains, with limited discussion of their combined impact on coastal management. Accordingly, this desk-based qualitative review discusses recent developments in Artificial Intelligence, Machine Learning, Deep Learning, and other data-driven techniques used for coastal management. In addition, it will identify future directions for such studies, especially with combined stressors. ------------------------------------------------------------------------------Cite this article: APA Style:Agarwala, N. (2027). Use of artificial intelligence and other data-driven technologies for coastal management. Maritime Technology and Research, 9(1), 289835. https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835 MDPI Style:Agarwala, N. Use of artificial intelligence and other data-driven technologies for coastal management. Marit. Technol. Res. 2027, 9, 289835. https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835 Vancouver Style:Agarwala N. (2027). Use of artificial intelligence and other data-driven technologies for coastal management. Marit. Technol. Res. 9(1):289835. https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835 ------------------------------------------------------------------------------ Highlights Provides an overview tying disparate AI\u002FML to cohesive coastal management frameworks Shows how coastal modeling has transitioned from traditional linear systems to modern multi-physics data-driven frameworks Shows how PINNs addresses AI limitations like hallucinations and physical anomalies Shows how AI optimizes hard gray infrastructure while scaling up soft green solutions Showcases how Decision Support Systems use spatial-temporal algorithms to balance ecosystem preservation with economic priorities","生物地球化学循环在沿海地区自然发生。河口、湿地和潮汐溪流对这些循环起到辅助作用。由于人类活动增加和自然发生的事件作为压力源，它们影响海洋空间中的物理、化学和生物过程，进而影响这些循环。这使得现有生态系统易受营养失衡的影响。因此，研究人员开始监测和绘制沿海环境，以了解不同压力源如何利用数值模型和经验模型影响这些生物地球化学过程。随着计算基础设施的进步，人工智能、机器学习和深度学习等数据驱动技术在不同水文系统中的应用已变得普遍。这推动了预测和决策框架在海岸管理中的应用，使栖息地保护更加科学。此类框架通过硬工程和软工程、基于自然的解决方案以及有管理的后退，平衡生态健康与人类活动。尽管关于人工智能和机器学习在沿海环境中应用的文献已相当丰富，但它们聚焦于独立领域，对其在海岸管理中的综合影响讨论有限。因此，本项基于案头的定性综述讨论了人工智能、机器学习、深度学习及其他用于海岸管理的数据驱动技术的最新进展。此外，它将确定此类研究的未来方向，尤其是在复合压力源方面。 ------------------------------------------------------------------------------ 引用本文： APA格式：Agarwala, N. (2027). Use of artificial intelligence and other data-driven technologies for coastal management. Maritime Technology and Research, 9(1), 289835. https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835 MDPI格式：Agarwala, N. Use of artificial intelligence and other data-driven technologies for coastal management. Marit. Technol. Res. 2027, 9, 289835. https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835 Vancouver格式：Agarwala N. (2027). Use of artificial intelligence and other data-driven technologies for coastal management. Marit. Technol. Res. 9(1):289835. https:\u002F\u002Fdoi.org\u002F10.33175\u002Fmtr.2027.289835 ------------------------------------------------------------------------------ 亮点 提供概述，将分散的人工智能\u002F机器学习与连贯的海岸管理框架联系起来 展示海岸建模如何转变","Maritime Technology and Research","2026-10-11T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":368},"该论文聚焦海岸带管理与海洋生态，属海洋与海事领域，与三农、农业信息化、智慧农业、数字乡村无直接关联，相关性门槛未通过。",[370],{"name":365,"url":362},[105,21,372,373],"数据驱动","海岸带管理",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":376},{"candidates":377,"agricultural_evidence":14,"reason":56},[378],{"code":32,"score":228,"supported":34,"title":379,"abstract":380,"tags":382},[36],[38,105,107,39,36,381],"decision support",[21,105],[384,385],"海岸带管理 人工智能 数据驱动 机器学习","海岸带管理 人工智能","海岸带管理人工智能数据驱动机器学习-4817","10.33175\u002Fmtr.2027.289835",{"doi":387,"openalex_id":389,"authors":390,"venue":365,"cited_by_count":15,"oa_url":394,"card":395,"direction":126,"ingested_from":78},"W7221479771",[391],{"name":392,"orcid":393},"Nitin Agarwala","https:\u002F\u002Forcid.org\u002F0000-0003-0916-3044","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FMTR\u002Farticle\u002Fdownload\u002F289835\u002F193722",{"tldr":396,"method":397,"finding":398,"direction":399,"opportunity":400},"综述人工智能与数据驱动技术在海岸带管理中的应用，并指出未来方向。","基于文献的定性综述，梳理AI、机器学习与深度学习在海岸环境建模中的应用。","现有研究多局限于独立领域，缺乏对多重压力下综合管理框架的整合讨论。","农业遥感与作物表型","可探索多压力源耦合下AI决策框架，并迁移至农业面源污染与滨海湿地协同治理。","2026-10-13T23:30:14.084085Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":90,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":409,"score_detail":410,"sources":413,"tags":415,"primary_topic":9,"secondary_topics":421,"classification":422,"search_phrases":461,"slug":464,"view_count":81,"doi":465,"paper":466,"created_at":481},4495,"A review of forest innovation: Technologies and sustainable practices for environmental conservation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.nxsust.2026.100546","Forests play a crucial role in climate regulation, biodiversity conservation, ecosystem service provision, and sustainable socio-economic development. However, increasing deforestation, climate change, habitat fragmentation, and unsustainable resource exploitation continue to threaten forest ecosystems worldwide. The objective of this review is to determine the ecological, economic, and governance conditions under which specific forest innovations translate into measurable sustainability outcomes, and to synthesize these conditions into coherent, evidence-based pathways applicable across different regional contexts. Framed through a social-ecological systems (SES) and resilience lens, this review critically examines recent advances in forest innovation by integrating sustainable forest management practices with emerging technological developments, including precision forestry, remote sensing, geographic information systems (GIS), LiDAR, unmanned aerial vehicles (UAVs), biotechnology, artificial intelligence (AI), digital forestry, biomass utilization, and forest-based bioeconomy approaches. Unlike conventional narrative reviews, this study applies a common five criterion evaluation framework comprising ecological effectiveness, technological maturity, economic feasibility, governance requirements, and regional applicability to comparatively evaluates the strengths, limitations, implementation challenges of these innovations across diverse ecological and socio-economic contexts, thereby moving beyond the largely descriptive, single-domain coverage of prior forest technology and forest governance reviews to an explicitly cross-domain, comparative synthesis. The review further explores the contributions of community-based forest management, climate-smart forestry, carbon management strategies, ecosystem services, green infrastructure, and innovative forest-derived products toward achieving long-term environmental sustainability. The analysis demonstrates that technological innovations alone cannot ensure sustainable forest management unless supported by effective governance, equitable community participation, adequate financial investment, and adaptive policy frameworks. The review identifies critical knowledge gaps related to technology adoption, institutional capacity, climate resilience, and long-term monitoring, and derives four specific, evidence-anchored integrated pathways that combine ecological, technological, economic, and governance innovations. This review provides a comprehensive, deliberately broad-scope synthesis of contemporary forest innovation and offers practical recommendations for researchers, policymakers, and forest managers working toward resilient forest ecosystems and the achievement of the United Nations Sustainable Development Goals, particularly SDG 15 (Life on Land).","森林在气候调节、生物多样性保护、生态系统服务供给以及可持续社会经济发展中发挥着至关重要的作用。然而，日益加剧的森林砍伐、气候变化、栖息地破碎化以及不可持续的资源开发持续威胁着全球森林生态系统。本综述旨在明确特定森林创新在何种生态、经济与治理条件下能够转化为可衡量的可持续性成果，并将这些条件综合为适用于不同区域背景的连贯且基于证据的路径。本综述以社会-生态系统（SES）和韧性为理论框架，通过将可持续森林管理实践与新兴技术发展相结合，批判性地审视了森林创新的最新进展，这些技术包括精准林业、遥感、地理信息系统（GIS）、激光雷达（LiDAR）、无人机（UAV）、生物技术、人工智能（AI）、数字林业、生物质利用以及基于森林的生物经济方法。与传统叙述性综述不同，本研究采用统一的五标准评价框架，包括生态有效性、技术成熟度、经济可行性、治理要求和区域适用性，对上述创新在不同生态和社会经济背景下的优势、局限及实施挑战进行比较评估，从而超越以往森林技术和森林治理综述 largely 描述性、单一领域的覆盖范围，实现明确的跨领域比较综合。本综述进一步探讨了社区森林管理、气候智慧型林业、碳管理策略、生态系统服务、绿色基础设施以及创新性林产品对实现长期环境可持续性的贡献。分析表明，除非得到有效治理、公平的社区参与、充足的财政投资和适应性政策框架的支持，否则仅靠技术创新无法确保可持续森林管理。本综述识别了与技术采用、机构能力、气候韧性和长期监测相关的关键知识空白，并推导出四条具体的、以证据为锚点的综合路径，这些路径融合了生态、技术、经济和治理创新。Th","Next Sustainability",77,{"impact":33,"substance":411,"depth":33,"authority":139,"freshness":315,"relevant":81,"comment":412},21,"该综述系统整合精准林业、遥感、AI等技术与治理条件，提出四条证据支撑路径，对智慧林业与数字乡村建设有较高参考价值，但属综述类论文、时效性一般，适合进入主题聚合而非头条精选。",[414],{"name":408,"url":405},[21,416,417,418,419,420],"智慧林业","遥感监测","森林碳汇","数字林业","可持续经营",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":423},{"candidates":424,"agricultural_evidence":14,"reason":56},[425,434,439,447,451,456],{"code":426,"score":140,"supported":34,"title":427,"abstract":428,"tags":433},"H6",[],[429,430,431,432],"生物多样性","生态系统服务","biodiversity","ecosystem service",[],{"code":242,"score":140,"supported":34,"title":435,"abstract":436,"tags":438},[],[245,246,437],"climate resilience",[],{"code":440,"score":140,"supported":34,"title":441,"abstract":442,"tags":446},"I4",[],[443,444,445],"地理信息","GIS","geographic information",[],{"code":49,"score":315,"supported":34,"title":448,"abstract":449,"tags":450},[],[53,54],[417],{"code":32,"score":452,"supported":34,"title":453,"abstract":454,"tags":455},4,[],[36],[21],{"code":457,"score":81,"supported":14,"title":458,"abstract":459,"tags":460},"H4",[],[],[418],[462,463],"森林创新 遥感 LiDAR 无人机","数字林业 森林碳汇 可持续经营","森林创新遥感LiDAR无人机-4495","10.1016\u002Fj.nxsust.2026.100546",{"doi":465,"openalex_id":467,"authors":468,"venue":408,"cited_by_count":15,"oa_url":475,"card":476,"direction":126,"ingested_from":78},"W7220854619",[469,472],{"name":470,"orcid":471},"Shivani Garg","https:\u002F\u002Forcid.org\u002F0000-0001-7879-7177",{"name":473,"orcid":474},"Pooja Arora","https:\u002F\u002Forcid.org\u002F0000-0002-4860-4714","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2949823626003016\u002Fpdf",{"tldr":477,"method":478,"finding":479,"direction":399,"opportunity":480},"综述森林创新技术，用五标准框架比较其可持续性成效与实施条件。","社会生态系统与韧性视角，五标准评估框架，综合遥感、AI、数字林业等。","技术创新需治理、社区参与、投资和适应性政策配合才能实现可持续森林管理。","可研究遥感\u002FAI在森林碳汇监测中的技术采纳障碍与制度适配路径。","2026-10-09T23:30:10.090218Z"]