[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4817":3,"related-4817":66},{"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":25,"classification":26,"search_phrases":45,"slug":48,"view_count":15,"doi":49,"paper":50,"created_at":65},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",null,"Maritime Technology and Research","2026-10-11T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"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。",[19],{"name":10,"url":6},[21,22,23,24],"机器学习","人工智能","数据驱动","海岸带管理",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":44},[32],{"code":33,"score":34,"supported":35,"title":36,"abstract":38,"tags":43},"I2",19,true,[37],"artificial intelligence",[39,21,40,41,37,42],"深度学习","machine learning","deep learning","decision support",[22,21],"农业相关性需要复核",[46,47],"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":49,"openalex_id":51,"authors":52,"venue":10,"cited_by_count":15,"oa_url":56,"card":57,"direction":63,"ingested_from":64},"W7221479771",[53],{"name":54,"orcid":55},"Nitin Agarwala","https:\u002F\u002Forcid.org\u002F0000-0003-0916-3044","https:\u002F\u002Fso04.tci-thaijo.org\u002Findex.php\u002FMTR\u002Farticle\u002Fdownload\u002F289835\u002F193722",{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"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。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:14.084085Z",{"total":67,"page":68,"page_size":67,"items":69},6,1,[70,174,244,300,392,467],{"id":71,"title":72,"url":73,"summary":74,"summary_zh":75,"content":9,"source_name":76,"source_url":73,"published_at":77,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":78,"score_detail":79,"sources":85,"tags":87,"primary_topic":92,"secondary_topics":98,"classification":110,"search_phrases":151,"slug":154,"view_count":15,"doi":155,"paper":156,"created_at":173},5558,"Estimation of Soil Water Content in Winter Wheat Field at Jointing Stage Using Multi-Source Features and Machine Learning","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16202071","Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R2 of 0.855, lowest MAE of 0.0212 cm3\u002Fcm3, and lowest RMSE of 0.0253 cm3\u002Fcm3. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.","准确、及时地监测土壤含水量（SWC）对作物灌溉管理和智慧农业实践具有重要的支撑作用。遥感技术为大规模农田土壤含水量估算或反演提供了快速便捷的手段。然而，现有机理建模方法受参数本地化和地表异质性限制，机器学习数据驱动方法则缺乏良好的物理解释性。针对单一数据驱动方法和单一植被微波散射模型的不足，并探究不同特征变量在机器学习中的估算效果，本研究提出了一种融合机器学习策略与多源特征的创新估算模型，所采用特征包括光谱指数和经典水云模型（WCM）衍生的特征。该模型的新颖性在于平衡物理解释性与数据驱动精度，即通过WCM提取相关土壤后向散射参数，再将其作为机器学习的输入特征。研究在冬小麦拔节期同步获取了田间实测数据与无人机（UAV）多源遥感数据，包括多光谱和合成孔径雷达（SAR）数据。通过提取SAR微波后向散射系数和光学植被指数等变量，并进行特征筛选和参数敏感性分析，优化了基于反向传播（BP）神经网络、支持向量回归（SVR）和随机森林（RF）算法的模型输入变量集。研究结果表明，所建立的估算模型具有优良的估算性能，尤其是RF算法，最高R²为0.855，最低MAE为0.0212 cm³\u002Fcm³，最低RMSE为0.0253 cm³\u002Fcm³。研究还表明，不同特征输入方案对土壤含水量估算性能有明显影响。总体而言，RF估算精度最佳，SVR次之，BPNN再次之。本研究证明，融合多源遥感数据与机器学习的模型能够提升冬小麦田土壤含水量估算能力，也可为华北平原冬小麦田土壤含水量监测提供一种有前景的方法。","Agronomy","2026-10-21T00:00:00Z",77,{"impact":80,"substance":81,"depth":82,"authority":83,"freshness":13,"relevant":68,"comment":84},16,21,17,13,"该研究融合多源遥感与机器学习估算冬小麦拔节期土壤含水量，方法有创新、结论可靠，对智慧灌溉有参考价值，但属细分领域论文，影响范围有限。",[86],{"name":76,"url":73},[88,21,89,90,91],"智慧农业","遥感监测","土壤墒情","冬小麦",{"code":93,"slug":94,"name":95,"path":96,"parent_code":97},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[99,105],{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":33,"slug":106,"name":107,"path":108,"parent_code":109},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":111,"locked":14,"version":28,"method":29,"evidence":112},"classified",{"candidates":113,"agricultural_evidence":35},[114,120,126,130,138,145],{"code":93,"score":115,"supported":35,"title":116,"abstract":118,"tags":119},26,[117,94],"winter wheat",[95,117,94],[91],{"code":100,"score":121,"supported":35,"title":122,"abstract":123,"tags":125},15,[101],[124,101],"土壤",[90],{"code":33,"score":121,"supported":35,"title":127,"abstract":128,"tags":129},[40],[21,40],[21],{"code":131,"score":13,"supported":35,"title":132,"abstract":133,"tags":137},"I3",[],[134,135,136],"遥感","remote sensing","multispectral",[89],{"code":139,"score":67,"supported":35,"title":140,"abstract":141,"tags":144},"H3",[],[142,143],"灌溉","irrigation",[],{"code":146,"score":147,"supported":35,"title":148,"abstract":149,"tags":150},"I1",4,[],[88],[88],[152,153],"冬小麦 土壤含水量 遥感","多源遥感 机器学习 墒情","冬小麦土壤含水量遥感-5558","10.3390\u002Fagronomy16202071",{"doi":155,"openalex_id":157,"authors":158,"venue":76,"cited_by_count":15,"oa_url":73,"card":168,"direction":63,"ingested_from":64},"W7223156204",[159,162,164,166],{"name":160,"orcid":161},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":163,"orcid":9},"Yi Zhang",{"name":165,"orcid":9},"WEI Lei",{"name":167,"orcid":9},"Chengcai Zhang",{"tldr":169,"method":170,"finding":171,"direction":61,"opportunity":172},"融合WCM物理特征与多源遥感，用机器学习估算冬小麦拔节期土壤含水量。","无人机多光谱与SAR数据，WCM参数+光谱指数，BP\u002FSVR\u002FRF建模。","RF最优，R²达0.855，RMSE为0.0253 cm³\u002Fcm³，特征方案影响显著。","可探索物理约束与深度学习耦合，并迁移至多生育期、多作物及不同土壤质地验证。","2026-10-22T11:20:18.887301Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":182,"sources":184,"tags":186,"primary_topic":189,"secondary_topics":190,"classification":196,"search_phrases":221,"slug":224,"view_count":15,"doi":225,"paper":226,"created_at":243},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":183},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[185],{"name":180,"url":177},[21,89,187,188],"土地利用","城市扩张",{"code":33,"slug":106,"name":107,"path":108,"parent_code":109},[191],{"code":192,"slug":193,"name":194,"path":195,"parent_code":109},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":111,"locked":14,"version":28,"method":29,"evidence":197},{"candidates":198,"agricultural_evidence":35},[199,203,211,216],{"code":33,"score":121,"supported":35,"title":200,"abstract":201,"tags":202},[40],[21,40],[21],{"code":192,"score":204,"supported":35,"title":205,"abstract":206,"tags":210},9,[],[207,208,209],"空间分析","GIS","spatial analysis",[],{"code":131,"score":212,"supported":35,"title":213,"abstract":214,"tags":215},7,[],[134,135],[89],{"code":217,"score":68,"supported":14,"title":218,"abstract":219,"tags":220},"H6",[],[],[187],[222,223],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":225,"openalex_id":227,"authors":228,"venue":180,"cited_by_count":15,"oa_url":177,"card":238,"direction":61,"ingested_from":64},"W7223022155",[229,232,234,236],{"name":230,"orcid":231},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":233,"orcid":9},"Goanar Jal Dup",{"name":235,"orcid":9},"Weyesa Merga Chemeda",{"name":237,"orcid":9},"Shankar Karuppannan",{"tldr":239,"method":240,"finding":241,"direction":61,"opportunity":242},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":245,"title":246,"url":247,"summary":248,"summary_zh":9,"content":9,"source_name":249,"source_url":9,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":251,"score_detail":252,"sources":257,"tags":259,"primary_topic":264,"secondary_topics":265,"classification":266,"search_phrases":286,"slug":289,"view_count":15,"doi":9,"paper":290,"created_at":299},5462,"Architecture diverse TabNet ensemble for crop type classification using harmonized multisource soil data（基于多源土壤数据融合的架构多样性TabNet集成作物类型分类）","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75366-7","对18,237条多源土壤记录、7类作物构建TabNet集成框架。TPE优化使对数损失由0.3325降至0.2566，集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。作者称尚未做外部验证。","Scientific Reports 2026-10-18","2026-10-18T00:00:00Z",70,{"impact":253,"substance":254,"depth":82,"authority":83,"freshness":255,"relevant":68,"comment":256},12,20,8,"方法新颖、数据规模可观且指标有实质提升，但缺乏外部验证、属细分方法研究，公共影响有限，可作候选而非必选。",[258],{"name":249,"url":247},[260,21,261,262,263],"农业人工智能","精准农业","土壤数据","作物分类",{"code":100,"slug":101,"name":102,"path":103,"parent_code":104},[],{"status":111,"locked":14,"version":28,"method":29,"evidence":267},{"candidates":268,"agricultural_evidence":35},[269,273,278,282],{"code":100,"score":254,"supported":35,"title":270,"abstract":271,"tags":272},[124,101],[124],[262],{"code":33,"score":274,"supported":14,"title":275,"abstract":276,"tags":277},2,[],[],[260,21],{"code":146,"score":68,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[261],{"code":131,"score":68,"supported":14,"title":283,"abstract":284,"tags":285},[],[],[263],[287,288],"TabNet 作物类型分类","多源土壤数据 作物分类","TabNet作物类型分类-5462",{"doi":9,"openalex_id":9,"authors":291,"venue":9,"cited_by_count":15,"oa_url":9,"card":292,"direction":296,"ingested_from":298},[],{"tldr":293,"method":294,"finding":295,"direction":296,"opportunity":297},"构建架构多样性TabNet集成，用多源土壤数据分类7类作物。","18,237条多源土壤记录，TPE调参的TabNet集成，对比XGBoost。","集成后准确率0.9053、宏F1 0.9025，优于XGBoost的0.8724。","农业人工智能与决策模型","尚未外部验证，可跨区域迁移验证并融合遥感时序特征提升泛化。","agent","2026-10-21T00:10:08.428584Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":314,"tags":316,"primary_topic":319,"secondary_topics":320,"classification":327,"search_phrases":365,"slug":368,"view_count":15,"doi":369,"paper":370,"created_at":391},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":310,"substance":311,"depth":310,"authority":312,"freshness":255,"relevant":68,"comment":313},18,22,14,"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。",[315],{"name":306,"url":303},[88,260,21,317,318],"食品安全","文献计量",{"code":33,"slug":106,"name":107,"path":108,"parent_code":109},[321],{"code":322,"slug":323,"name":324,"path":325,"parent_code":326},"E4","food-safety","质量安全与溯源","\u002Ftopic\u002Ffood-supply\u002Ffood-safety","E",{"status":111,"locked":14,"version":28,"method":29,"evidence":328},{"candidates":329,"agricultural_evidence":35},[330,336,341,347,354,361],{"code":33,"score":34,"supported":35,"title":331,"abstract":332,"tags":335},[37],[39,21,333,40,41,37,334],"计算机视觉","computer vision",[260,21],{"code":322,"score":121,"supported":35,"title":337,"abstract":339,"tags":340},[338],"food safety",[317,338],[317],{"code":342,"score":121,"supported":35,"title":343,"abstract":345,"tags":346},"I7",[344],"bibliometric",[318,344],[318],{"code":348,"score":67,"supported":35,"title":349,"abstract":350,"tags":353},"H7",[],[351,352],"气候变化","climate change",[],{"code":355,"score":356,"supported":35,"title":357,"abstract":358,"tags":360},"A8",3,[],[359],"food security",[],{"code":146,"score":68,"supported":14,"title":362,"abstract":363,"tags":364},[],[],[88],[366,367],"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":369,"openalex_id":371,"authors":372,"venue":306,"cited_by_count":15,"oa_url":9,"card":386,"direction":296,"ingested_from":64},"W7222920742",[373,375,377,380,383],{"name":374,"orcid":9},"Harang Lee",{"name":376,"orcid":9},"지은서",{"name":378,"orcid":379},"Yoonbin Kim","https:\u002F\u002Forcid.org\u002F0000-0002-5625-2560",{"name":381,"orcid":382},"Joon-Goo Lee","https:\u002F\u002Forcid.org\u002F0000-0003-3617-5518",{"name":384,"orcid":385},"Woo‐Ju Kim","https:\u002F\u002Forcid.org\u002F0000-0002-6954-1580",{"tldr":387,"method":388,"finding":389,"direction":296,"opportunity":390},"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。","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":393,"title":394,"url":395,"summary":396,"summary_zh":397,"content":9,"source_name":398,"source_url":395,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":399,"score_detail":400,"sources":402,"tags":404,"primary_topic":409,"secondary_topics":412,"classification":413,"search_phrases":431,"slug":434,"view_count":15,"doi":435,"paper":436,"created_at":466},5399,"Spatial Heterogeneity and Nonlinear Environmental Associations of Positive Climatic Moisture Deficit with Implications for Sustainable Water Resource Assessment: A Case Study of Anyang","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu182010595","Positive climatic moisture deficit (CMD+) characterizes the seasonal imbalance between precipitation supply and atmospheric water demand, yet its spatial heterogeneity and associations with environmental and human-related factors remain insufficiently understood. Taking Anyang, China, as a case study, we calculated annual CMD+ for 2000–2020 using 1 km monthly precipitation and potential evapotranspiration data and applied an extreme gradient boosting (XGBoost)–SHapley Additive exPlanations (SHAP) framework to the corresponding long-term mean fields to examine climatological spatial heterogeneity. Five predictors were considered, including elevation, normalized difference vegetation index (NDVI), irrigation fraction, built-up fraction, and population density. Nested spatial-block cross-validation showed good model generalization. Long-term mean CMD+ was generally lower in the western mountainous area and higher across the central and eastern plains. The digital elevation model (DEM) accounted for 66.10% of the normalized mean absolute SHAP importance, followed by NDVI (13.47%) and irrigation fraction (9.72%). A DEM-excluded sensitivity model retained a spatial cross-validated R2 of 0.781, compared with 0.898 for the full model. SHAP dependence analysis revealed pronounced nonlinear responses, with primary local model response transition points at approximately 297.1 m for DEM, 0.812 for NDVI, and 23.1% for irrigation fraction. The strongest pairwise SHAP interaction occurred between NDVI and irrigation fraction, accounting for 21.87% of total pairwise interaction strength. These results show that the spatial heterogeneity of CMD+ in Anyang is closely associated with topographic background, while vegetation status and irrigated agriculture provide additional nonlinear and interactive explanatory information. The identified spatial patterns and model-response transitions provide spatial information for understanding regional climatic moisture deficits and for subsequent sustainable water resource assessment.","正向气候水分亏缺（CMD+）刻画了降水供给与大气水分需求之间的季节性失衡，但其空间异质性及其与环境因素和人为因素的关联仍缺乏充分认识。以中国安阳为案例区，本研究利用1 km月降水量与潜在蒸散量数据计算了2000—2020年的年CMD+，并将极端梯度提升（XGBoost）—SHapley加性解释（SHAP）框架应用于相应的长期均值场，以考察气候态空间异质性。研究考虑了5个预测因子，包括高程、归一化植被指数（NDVI）、灌溉面积比例、建成区比例和人口密度。嵌套空间分块交叉验证表明模型具有良好的泛化能力。长期平均CMD+总体表现为西部山区较低、中部和东部平原较高。数字高程模型（DEM）占归一化平均绝对SHAP重要性的66.10%，其次为NDVI（13.47%）和灌溉面积比例（9.72%）。剔除DEM的敏感性模型保持了0.781的空间交叉验证R²，而完整模型为0.898。SHAP依赖分析揭示了显著的非线性响应，主要局部模型响应转变点分别约为DEM 297.1 m、NDVI 0.812和灌溉面积比例23.1%。最强的成对SHAP交互作用出现在NDVI与灌溉面积比例之间，占总成对交互强度的21.87%。结果表明，安阳CMD+的空间异质性与地形背景密切相关，而植被状况和灌溉农业提供了额外的非线性和交互解释信息。所识别的空间格局和模型响应转变点为理解区域气候水分亏缺及后续可持续水资源评估提供了空间信息。","Sustainability",64,{"impact":255,"substance":310,"depth":80,"authority":83,"freshness":204,"relevant":68,"comment":401},"以安阳为案例，用遥感与XGBoost-SHAP揭示气候水分亏缺的空间异质性，方法有新意、结论可靠，但属区域案例研究，公共影响有限。",[403],{"name":398,"url":395},[405,21,406,407,408],"农业遥感","灌溉管理","水资源评估","气候干旱",{"code":139,"slug":143,"name":410,"path":411,"parent_code":104},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",[],{"status":111,"locked":14,"version":28,"method":29,"evidence":414},{"candidates":415,"agricultural_evidence":35},[416,423,427],{"code":139,"score":310,"supported":35,"title":417,"abstract":419,"tags":422},[418],"water resource",[142,420,143,418,421],"水资源","evapotranspiration",[406],{"code":33,"score":68,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[21],{"code":131,"score":68,"supported":14,"title":428,"abstract":429,"tags":430},[],[],[405],[432,433],"安阳 气候水分亏缺 遥感","XGBoost SHAP 水资源评估","安阳气候水分亏缺遥感-5399","10.3390\u002Fsu182010595",{"doi":435,"openalex_id":437,"authors":438,"venue":398,"cited_by_count":15,"oa_url":395,"card":460,"direction":465,"ingested_from":64},"W7222973420",[439,441,444,447,450,452,454,456,458],{"name":440,"orcid":9},"Yan Li",{"name":442,"orcid":443},"Xiaohua Yang","https:\u002F\u002Forcid.org\u002F0000-0001-8387-5213",{"name":445,"orcid":446},"Dehui Bian","https:\u002F\u002Forcid.org\u002F0000-0001-6921-1875",{"name":448,"orcid":449},"Qi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3156-9246",{"name":451,"orcid":9},"Lequn Gu",{"name":453,"orcid":9},"Ailing Zhang",{"name":455,"orcid":9},"Wei Gao",{"name":457,"orcid":9},"Wenjie Qi",{"name":459,"orcid":9},"Yayun Ma",{"tldr":461,"method":462,"finding":463,"direction":61,"opportunity":464},"以安阳为例，用XGBoost-SHAP分析2000—2020年气候水分亏缺的空间异质性及其环境关联。","1 km月降水与潜在蒸散数据计算CMD+，结合XGBoost-SHAP与嵌套空间","CMD+西部山区低、中东部平原高；高程贡献66.1%，NDVI与灌溉比例呈显著非线性交互。","可引入多源遥感与作物种植结构，探究CMD+非线性阈值对灌溉需水与水资源配置的传导机制。","数字乡村与农业信息化","2026-10-20T23:30:23.927575Z",{"id":468,"title":469,"url":470,"summary":471,"summary_zh":472,"content":9,"source_name":473,"source_url":474,"published_at":475,"category":12,"cover_url":9,"hotness":476,"is_selected":14,"score":477,"score_detail":478,"sources":480,"tags":484,"primary_topic":487,"secondary_topics":488,"classification":489,"search_phrases":517,"slug":520,"view_count":15,"doi":521,"paper":522,"created_at":532},5322,"MACHINE LEARNING BASED PREDICTION OF HYDROGEOLOGICAL PARAMETERS AND GROUNDWATER POTENTIAL INDEX USING GEOSPATIAL AND BOREHOLE DATA","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23434956","Abstract: Groundwater is a critical natural resource for domestic consumption, agricultural production, industrial development, and ecological sustainability. Accurate prediction of hydrogeological parameters and groundwater potential is essential for sustainable groundwater management, particularly in regions experiencing irregular rainfall, increasing water demand, and declining groundwater levels. This research paper presents a machine learning-based framework for predicting hydrogeological parameters and developing a Groundwater Potential Index (GWPI) by integrating geospatial datasets with borehole records. The proposed approach combines terrain characteristics, geological formations, soil properties, land use and land cover, drainage density, rainfall, slope, and remotely sensed indices with borehole-derived information, including water-table depth, aquifer thickness, lithology, and well yield. Machine learning algorithms such as Random Forest, Support Vector Regression, Gradient Boosting, and Artificial Neural Networks can be employed to estimate hydrogeological variables and identify areas with different groundwater potential levels. The predicted parameters are integrated within a geospatial framework to generate a spatially distributed groundwater potential index. Model performance can be assessed using the coefficient of determination (\\(R^2\\)), root mean square error (RMSE), and mean absolute error (MAE). The proposed framework offers a reproducible and scalable approach for groundwater exploration, borehole siting, and regional water-resource planning. Since no study-area dataset or model outputs have been provided, the paper presents a methodological framework rather than claiming experimentally validated results. Keywords: Machine Learning, Groundwater Potential Index, Hydrogeological Parameters, Geospatial Analysis, Borehole Data, Random Forest, Remote Sensing, Groundwater Management.","摘要：地下水是生活用水、农业生产、工业发展和生态可持续性的关键自然资源。准确预测水文地质参数和地下水潜力对于可持续地下水管理至关重要，尤其是在降雨不规律、用水需求不断增加和地下水位持续下降的地区。本文提出了一种基于机器学习的框架，通过将地理空间数据集与钻孔记录相结合，预测水文地质参数并构建地下水潜力指数（GWPI）。所提方法综合了地形特征、地质构造、土壤性质、土地利用与土地覆盖、河网密度、降雨、坡度以及遥感指数，并结合钻孔衍生信息，包括水位埋深、含水层厚度、岩性和井出水量。可采用随机森林、支持向量回归、梯度提升和人工神经网络等机器学习算法估算水文地质变量，并识别具有不同地下水潜力水平的区域。预测参数在地理空间框架内进行集成，以生成空间分布的地下水潜力指数。模型性能可通过决定系数（\\(R^2\\)）、均方根误差（RMSE）和平均绝对误差（MAE）进行评估。所提框架为地下水勘探、钻孔选址和区域水资源规划提供了一种可重复且可扩展的方法。由于未提供研究区数据集或模型输出，本文呈现的是方法学框架，而非声称经过实验验证的结果。关键词：机器学习，地下水潜力指数，水文地质参数，地理空间分析，钻孔数据，随机森林，遥感，地下水管理。","ACCENT JOURNAL OF  ECONOMICS ECOLOGY  & ENGINEERING","https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fview\u002F6754","2026-10-17T00:00:00Z",25,47,{"impact":255,"substance":253,"depth":312,"authority":147,"freshness":204,"relevant":68,"comment":479},"方法框架类论文，无实测数据与验证结果，主题与农业水资源相关但公共价值有限，不宜进入每日精选。",[481,482],{"name":473,"url":474},{"name":473,"url":483},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[21,134,485,486],"水资源管理","地下水监测",{"code":33,"slug":106,"name":107,"path":108,"parent_code":109},[],{"status":111,"locked":14,"version":28,"method":29,"evidence":490},{"candidates":491,"agricultural_evidence":35},[492,496,500,504,508,512],{"code":33,"score":121,"supported":35,"title":493,"abstract":494,"tags":495},[40],[21,40],[21],{"code":131,"score":212,"supported":35,"title":497,"abstract":498,"tags":499},[],[134,135],[134],{"code":100,"score":67,"supported":35,"title":501,"abstract":502,"tags":503},[],[124,101],[],{"code":139,"score":147,"supported":35,"title":505,"abstract":506,"tags":507},[],[420],[485],{"code":192,"score":356,"supported":35,"title":509,"abstract":510,"tags":511},[],[207],[],{"code":342,"score":356,"supported":35,"title":513,"abstract":514,"tags":516},[],[515],"methodological framework",[],[518,519],"机器学习 地下水潜力指数","钻孔数据 水文地质参数","机器学习地下水潜力指数-5322","10.5281\u002Fzenodo.23434956",{"doi":521,"openalex_id":523,"authors":524,"venue":473,"cited_by_count":15,"oa_url":474,"card":527,"direction":61,"ingested_from":64},"W7222805722",[525],{"name":526,"orcid":9},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":528,"method":529,"finding":530,"direction":61,"opportunity":531},"提出融合地理空间与钻孔数据的机器学习框架，预测水文地质参数与地下水潜力指数。","随机森林、SVR、梯度提升、神经网络结合遥感与钻孔数据建模。","构建了可复现的地下水潜力评估方法框架，尚未经实测数据验证。","可将该框架落地到具体农业区，用实测钻孔数据验证并耦合灌溉需水预测。","2026-10-19T23:30:22.978385Z"]