[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4526":3,"related-4526":111},{"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":23,"tags":28,"primary_topic":34,"secondary_topics":40,"classification":41,"search_phrases":80,"slug":83,"view_count":21,"doi":84,"paper":85,"created_at":110},4526,"Explainable machine learning for spatial modelling of groundwater nitrate contamination: a multi-year analysis in the Duero basin","https:\u002F\u002Fdoi.org\u002F10.6084\u002Fm9.figshare.34244894.v1","Groundwater nitrate contamination is a major water-quality concern, with implications for drinking water supplies and groundwater-dependent ecosystems. This study assesses the Duero River basin (Spain), where groundwater nitrate concentrations have increased in recent decades due to intensive agriculture and livestock farming. The probability of elevated groundwater nitrate concentrations was predicted using machine learning, formulated as a binary classification problem with a threshold of 37.5 mg\u002FL. Monitoring data were combined with spatially derived environmental and anthropogenic predictors. Several tree-based algorithms were evaluated, with Random Forest selected for its consistent predictive performance. Model performance was assessed through repeated nested cross-validation and the use of metrics suitable for imbalanced datasets, achieving an AUC of 0.79-0.88 and an F1-score of 0.56-0.70 for the minority class across the three analysed hydrological years. SHAP analysis identified precipitation, diffuse agricultural pressures, distance to surface water bodies, NDVI, and soil properties as the main contributing predictors. Multi-year probability maps showed substantial spatial coherence with officially designated Nitrate Vulnerable Zones (46.9-64.9%), while identifying additional high-probability areas outside them (12.7-23.6%). The integration of explainable machine learning, multi-year spatial modelling, and regulatory comparison provides a framework to support groundwater monitoring, review vulnerable-zone delineation, and prioritise mitigation measures.","地下水硝酸盐污染是一个重要的水质问题，对饮用水供应和依赖地下水的生态系统具有影响。本研究评估了西班牙杜埃罗河流域（Duero River basin），近几十年来，由于集约化农业和畜牧业，该地区地下水硝酸盐浓度有所上升。本研究使用机器学习预测地下水硝酸盐浓度升高的概率，并将其表述为以37.5 mg\u002FL为阈值的二分类问题。监测数据与空间衍生的环境及人为预测因子相结合。研究评估了多种基于树的算法，并因随机森林（Random Forest）具有稳定的预测性能而选用该算法。模型性能通过重复嵌套交叉验证以及适用于不平衡数据集的指标进行评估，在三个分析水文年度中，少数类别的AUC达到0.79-0.88，F1分数达到0.56-0.70。SHAP分析确定降水、面源农业压力、距地表水体距离、NDVI和土壤属性为主要贡献预测因子。多年概率图显示出与官方指定硝酸盐脆弱区（Nitrate Vulnerable Zones）较高的空间一致性（46.9-64.9%），同时在这些区域之外识别出额外的高概率区域（12.7-23.6%）。可解释机器学习、多年空间建模和监管比较的整合提供了一个框架，可支持地下水监测、审查脆弱区划定并优先安排缓解措施。",null,"Figshare","2026-10-08T00:00:00Z","论文",25,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,12,9,1,"可解释机器学习结合多年空间建模识别地下水硝酸盐污染高风险区，方法新颖、结论可靠，对农业面源污染监测与脆弱区划定有参考价值。",[24,25],{"name":10,"url":6},{"name":26,"url":27},"International Journal of River Basin Management","https:\u002F\u002Fdoi.org\u002F10.1080\u002F15715124.2026.2742495",[29,30,31,32,33],"可解释机器学习","农业面源污染","遥感监测","地下水硝酸盐","硝酸盐脆弱区",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":42,"locked":14,"version":43,"method":44,"evidence":45},"classified","2026-10-22.2","rules",{"candidates":46,"agricultural_evidence":49},[47,55,63,70,75],{"code":35,"score":48,"supported":49,"title":50,"abstract":52,"tags":54},15,true,[51],"machine learning",[53,51],"机器学习",[29],{"code":56,"score":57,"supported":49,"title":58,"abstract":59,"tags":62},"C",6,[],[60,61],"畜牧","livestock",[],{"code":64,"score":57,"supported":49,"title":65,"abstract":66,"tags":69},"H2",[],[67,68],"土壤","soil",[],{"code":71,"score":21,"supported":14,"title":72,"abstract":73,"tags":74},"H5",[],[],[30],{"code":76,"score":21,"supported":14,"title":77,"abstract":78,"tags":79},"I3",[],[],[31],[81,82],"SHAP 硝酸盐脆弱区","可解释机器学习 农业面源污染 地下水硝酸盐 硝酸盐脆弱区","SHAP硝酸盐脆弱区-4526","10.6084\u002Fm9.figshare.34244894.v1",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":101,"oa_url":6,"card":102,"direction":108,"ingested_from":109},"W7220971680",[88,91,93,95,98],{"name":89,"orcid":90},"Manuel Rodríguez del Rosario","https:\u002F\u002Forcid.org\u002F0000-0002-4202-6722",{"name":92,"orcid":9},"Victor Gómez-Escalonilla",{"name":94,"orcid":9},"Héctor Aguilera",{"name":96,"orcid":97},"Pedro Martínez‐Santos","https:\u002F\u002Forcid.org\u002F0000-0003-2014-4063",{"name":99,"orcid":100},"África de la Hera Portillo","https:\u002F\u002Forcid.org\u002F0000-0001-7821-1834",0,{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"用可解释机器学习预测西班牙杜埃罗盆地地下水硝酸盐超标概率并绘制多年风险图。","随机森林二分类，结合监测数据与空间环境人为预测因子，SHAP解释。","模型AUC达0.79-0.88，识别出硝酸盐脆弱区外12.7-23.6%的高风险区。","农业绿色发展与碳","可迁移至其他农业流域，结合时序遥感与政策情景模拟优化脆弱区动态划定。","农业人工智能与决策模型","openalex","2026-10-09T23:30:47.673746Z",{"total":57,"page":21,"page_size":57,"items":112},[113,209,278,357,440,512],{"id":114,"title":115,"url":116,"summary":117,"summary_zh":118,"content":9,"source_name":119,"source_url":116,"published_at":120,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":122,"score_detail":123,"sources":129,"tags":131,"primary_topic":135,"secondary_topics":141,"classification":147,"search_phrases":184,"slug":187,"view_count":101,"doi":188,"paper":189,"created_at":208},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",10,77,{"impact":124,"substance":125,"depth":126,"authority":127,"freshness":121,"relevant":21,"comment":128},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。",[130],{"name":119,"url":116},[132,53,31,133,134],"智慧农业","土壤墒情","冬小麦",{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},"A2","wheat","小麦","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[142,146],{"code":64,"slug":68,"name":143,"path":144,"parent_code":145},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":148},{"candidates":149,"agricultural_evidence":49},[150,156,160,164,171,178],{"code":136,"score":151,"supported":49,"title":152,"abstract":154,"tags":155},26,[153,137],"winter wheat",[138,153,137],[134],{"code":64,"score":48,"supported":49,"title":157,"abstract":158,"tags":159},[68],[67,68],[133],{"code":35,"score":48,"supported":49,"title":161,"abstract":162,"tags":163},[51],[53,51],[53],{"code":76,"score":121,"supported":49,"title":165,"abstract":166,"tags":170},[],[167,168,169],"遥感","remote sensing","multispectral",[31],{"code":172,"score":57,"supported":49,"title":173,"abstract":174,"tags":177},"H3",[],[175,176],"灌溉","irrigation",[],{"code":179,"score":180,"supported":49,"title":181,"abstract":182,"tags":183},"I1",4,[],[132],[132],[185,186],"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":188,"openalex_id":190,"authors":191,"venue":119,"cited_by_count":101,"oa_url":116,"card":201,"direction":207,"ingested_from":109},"W7223156204",[192,195,197,199],{"name":193,"orcid":194},"Mingliang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-6486-6087",{"name":196,"orcid":9},"Yi Zhang",{"name":198,"orcid":9},"WEI Lei",{"name":200,"orcid":9},"Chengcai Zhang",{"tldr":202,"method":203,"finding":204,"direction":205,"opportunity":206},"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。","农业遥感与作物表型","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。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:18.887301Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":101,"score_detail":217,"sources":219,"tags":221,"primary_topic":224,"secondary_topics":225,"classification":231,"search_phrases":255,"slug":258,"view_count":101,"doi":259,"paper":260,"created_at":277},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":101,"substance":101,"depth":101,"authority":101,"freshness":101,"relevant":101,"comment":218},"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。",[220],{"name":215,"url":212},[53,31,222,223],"土地利用","城市扩张",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[226],{"code":227,"slug":228,"name":229,"path":230,"parent_code":39},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":42,"locked":14,"version":43,"method":44,"evidence":232},{"candidates":233,"agricultural_evidence":49},[234,238,245,250],{"code":35,"score":48,"supported":49,"title":235,"abstract":236,"tags":237},[51],[53,51],[53],{"code":227,"score":20,"supported":49,"title":239,"abstract":240,"tags":244},[],[241,242,243],"空间分析","GIS","spatial analysis",[],{"code":76,"score":246,"supported":49,"title":247,"abstract":248,"tags":249},7,[],[167,168],[31],{"code":251,"score":21,"supported":14,"title":252,"abstract":253,"tags":254},"H6",[],[],[222],[256,257],"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":259,"openalex_id":261,"authors":262,"venue":215,"cited_by_count":101,"oa_url":212,"card":272,"direction":205,"ingested_from":109},"W7223022155",[263,266,268,270],{"name":264,"orcid":265},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":267,"orcid":9},"Goanar Jal Dup",{"name":269,"orcid":9},"Weyesa Merga Chemeda",{"name":271,"orcid":9},"Shankar Karuppannan",{"tldr":273,"method":274,"finding":275,"direction":205,"opportunity":276},"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":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":286,"score_detail":287,"sources":291,"tags":293,"primary_topic":298,"secondary_topics":303,"classification":305,"search_phrases":329,"slug":332,"view_count":21,"doi":333,"paper":334,"created_at":356},5193,"Explainable machine learning with a graphical user interface for estimating compressive strength and life cycle assessment based embodied carbon of rice husk ash concrete","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42452-026-09656-0","Meeting the growing demand for concrete while reducing its environmental impact remains a crucial challenge for the construction industry. Although rice husk ash (RHA) offers substantial potential as a supplementary cementitious material to lower cement-related emissions, its widespread adoption is constrained by uncertainty in achieving a performance-environmental efficiency trade-off between mechanical performance and embodied carbon reduction. This paper proposes an explainable machine learning (ML) framework for the evaluation of compressive strength of RHA-based concrete across various curing stages, complemented by life cycle assessment (LCA) based embodied carbon evaluation. A refined database compiled from existing studies was adopted to train and optimise eight different ML models. The models were optimised using Optuna and assessed using multiple performance metrics, while SHapley Additive exPlanation (SHAP) was adopted to quantify global and local feature contributions. To support practical deployment, the best performing models were incorporated within an interactive decision support interface facilitating real time, coding free compressive strength prediction together with LCA-based embodied carbon calculation; thereby supporting performance-based mix design and accelerating the sustainable use of agricultural waste derived cementitious materials.","在满足日益增长的混凝土需求的同时降低其环境影响，仍是建筑行业面临的关键挑战。尽管稻壳灰（rice husk ash, RHA）作为辅助胶凝材料在降低水泥相关排放方面具有巨大潜力，但其广泛应用受到力学性能与隐含碳减排之间性能-环境效率权衡不确定性的制约。本文提出了一种可解释机器学习（machine learning, ML）框架，用于评估稻壳灰基混凝土在不同养护阶段下的抗压强度，并辅以基于生命周期评价（life cycle assessment, LCA）的隐含碳评估。采用从现有研究中汇编的精细化数据库训练并优化了八种不同的机器学习模型。模型通过Optuna进行优化，并采用多项性能指标进行评估，同时采用SHapley加性解释（SHapley Additive exPlanation, SHAP）量化全局与局部特征贡献。为支持实际应用，将性能最优的模型集成于交互式决策支持界面中，实现实时、免编程的抗压强度预测及基于生命周期评价的隐含碳计算，从而支持基于性能的配合比设计，并加速农业废弃物衍生胶凝材料的可持续利用。","Discover Applied Sciences","2026-10-15T00:00:00Z",69,{"impact":19,"substance":288,"depth":126,"authority":19,"freshness":289,"relevant":21,"comment":290},20,8,"将稻壳灰混凝土的抗压强度预测与隐含碳评估整合为可解释机器学习框架，并配有图形界面，方法新颖、数据扎实，对农业废弃物高值化利用有参考价值，但属专业论文，公共影响范围有限。",[292],{"name":284,"url":281},[29,294,295,296,297],"生命周期评价","农业废弃物利用","稻壳灰混凝土","低碳建材",{"code":299,"slug":300,"name":301,"path":302,"parent_code":140},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[304],{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},{"status":42,"locked":14,"version":43,"method":44,"evidence":306},{"candidates":307,"agricultural_evidence":49},[308,313,318,324],{"code":35,"score":17,"supported":49,"title":309,"abstract":310,"tags":312},[51],[53,51,311],"decision support",[29],{"code":299,"score":314,"supported":49,"title":315,"abstract":316,"tags":317},11,[300],[300],[],{"code":71,"score":246,"supported":49,"title":319,"abstract":320,"tags":323},[],[321,322],"农业废弃物","agricultural waste",[295],{"code":325,"score":21,"supported":14,"title":326,"abstract":327,"tags":328},"H4",[],[],[294],[330,331],"稻壳灰混凝土 抗压强度","稻壳灰 生命周期评价","稻壳灰混凝土抗压强度-5193","10.1007\u002Fs42452-026-09656-0",{"doi":333,"openalex_id":335,"authors":336,"venue":284,"cited_by_count":101,"oa_url":281,"card":351,"direction":108,"ingested_from":109},"W7222284745",[337,340,343,346,348],{"name":338,"orcid":339},"Stephen Adeyemi Alabi","https:\u002F\u002Forcid.org\u002F0000-0001-8731-6961",{"name":341,"orcid":342},"Ibrahim A. Tijani","https:\u002F\u002Forcid.org\u002F0000-0002-9423-1074",{"name":344,"orcid":345},"Alireza Moghayedi","https:\u002F\u002Forcid.org\u002F0000-0002-4165-0777",{"name":347,"orcid":9},"Jeffrey Mahachi",{"name":349,"orcid":350},"Samson Olalekan Odeyemi","https:\u002F\u002Forcid.org\u002F0000-0001-5217-3403",{"tldr":352,"method":353,"finding":354,"direction":106,"opportunity":355},"用可解释机器学习加图形界面预测稻壳灰混凝土抗压强度并评估隐含碳。","八种机器学习模型经Optuna优化，SHAP解释，结合生命周期评价与交互界面。","最优模型可实时无代码预测强度并算隐含碳，支撑性能导向配合比设计。","可将稻壳灰混凝土的强度-碳权衡模型扩展到其他农业废弃物建材并接入乡村低碳建造决策。","2026-10-17T23:30:53.298664Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":363,"source_url":360,"published_at":285,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":373,"secondary_topics":374,"classification":380,"search_phrases":424,"slug":427,"view_count":21,"doi":428,"paper":429,"created_at":439},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)",70,{"impact":17,"substance":124,"depth":48,"authority":19,"freshness":20,"relevant":21,"comment":366},"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[368],{"name":363,"url":360},[132,370,371,372,31],"农业人工智能","可持续发展","气候变化",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[375],{"code":376,"slug":377,"name":378,"path":379,"parent_code":145},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"status":42,"locked":14,"version":43,"method":44,"evidence":381},{"candidates":382,"agricultural_evidence":49},[383,390,395,401,405,411,417],{"code":35,"score":17,"supported":49,"title":384,"abstract":386,"tags":389},[385],"artificial intelligence",[387,53,51,388,385],"深度学习","deep learning",[370],{"code":376,"score":48,"supported":49,"title":391,"abstract":393,"tags":394},[392],"climate change",[372,392],[372],{"code":179,"score":246,"supported":49,"title":396,"abstract":397,"tags":400},[],[398,399],"精准农业","precision agriculture",[132],{"code":76,"score":246,"supported":49,"title":402,"abstract":403,"tags":404},[],[167,168],[31],{"code":325,"score":57,"supported":49,"title":406,"abstract":407,"tags":410},[],[408,409],"碳排放","carbon emission",[],{"code":251,"score":57,"supported":49,"title":412,"abstract":413,"tags":416},[],[414,415],"生物多样性","biodiversity",[],{"code":418,"score":57,"supported":49,"title":419,"abstract":420,"tags":423},"I5",[],[421,422],"物联网","internet of things",[],[425,426],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":428,"openalex_id":430,"authors":431,"venue":363,"cited_by_count":101,"oa_url":360,"card":434,"direction":207,"ingested_from":109},"W7222092855",[432],{"name":433,"orcid":9},"Deepmala Suryavanshi",{"tldr":435,"method":436,"finding":437,"direction":106,"opportunity":438},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","2026-10-16T23:30:15.059867Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":447,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":15,"score_detail":448,"sources":451,"tags":453,"primary_topic":457,"secondary_topics":458,"classification":459,"search_phrases":478,"slug":481,"view_count":21,"doi":482,"paper":483,"created_at":511},4915,"Modeling runoff, sediment, and nutrient dynamics in the Ganjiang River Basin, China: A hierarchical SWAT-BiLSTM framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jwpe.2026.111080","Rivers and lakes are vital to regional water security and ecosystem stability, yet the integrated simulation of hydrological, erosion and pollution processes remains constrained by the complex interplay between climate change and human activities. To address systematic biases arising from oversimplified mechanisms and strong non-linearities, this study proposes a hierarchical hybrid framework that sequentially feeds SWAT-simulated hydrological and water-quality processes into a BiLSTM network for residual correction, achieving coordinated, high-precision simulations of runoff, sediment and nutrient fluxes in the Ganjiang River Basin. Validated against multi-source data that hydrological data during 2011–2022 and water quality data during 2021–2022, the SWAT-BiLSTM outperformed standalone SWAT across all metrics, primarily driven by precision gains and residual mitigation rather than computational speedup. Specifically, the average R 2 increased by 6%–9% for runoff, 6%–19% for sediment and significantly by 9%–42% for nutrients, with a maximum P BIAS reduction of 67.4%. The hybrid model further revealed the cascade response mechanisms of the hydrological-erosion-pollution triad, highlighting concentrated rainfall and agricultural cycles as key drivers of seasonal nutrient peaks. These findings confirm the efficacy of deep learning-enhanced mechanistic modeling, offering robust decision support for basin-scale environmental management and pollution control.","河流与湖泊对区域水安全与生态系统稳定至关重要，然而水文、侵蚀与污染过程的集成模拟仍受制于气候变化与人类活动之间复杂的相互作用。为解决机制过度简化与强非线性所导致的系统性偏差，本研究提出了一种分层混合框架，将SWAT模拟的水文与水质过程依次输入BiLSTM网络进行残差校正，从而实现对赣江流域径流、泥沙与营养盐通量的协同高精度模拟。基于多源数据（2011—2022年水文数据及2021—2022年水质数据）的验证表明，SWAT-BiLSTM在所有指标上均优于单一SWAT模型，其优势主要源于精度提升与残差削减，而非计算速度的加快。具体而言，径流平均R²提高了6%—9%，泥沙提高了6%—19%，营养盐显著提高了9%—42%，PBIAS最大降低幅度达67.4%。该混合模型进一步揭示了水文—侵蚀—污染三元体系的级联响应机制，突出表明集中降雨与农业周期是季节性营养盐峰值的关键驱动因素。这些发现证实了深度学习增强机理模型的有效性，为流域尺度环境管理与污染控制提供了有力的决策支持。","Journal of Water Process Engineering","2026-10-13T00:00:00Z",{"impact":124,"substance":18,"depth":17,"authority":449,"freshness":20,"relevant":21,"comment":450},14,"提出SWAT-BiLSTM分层混合框架，显著提升径流、泥沙与营养盐模拟精度，对流域农业面源污染治理有实质参考价值。",[452],{"name":446,"url":443},[387,30,454,455,456],"智慧水利","水文模型","赣江流域",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[],{"status":42,"locked":14,"version":43,"method":44,"evidence":460},{"candidates":461,"agricultural_evidence":49},[462,466,470,474],{"code":35,"score":121,"supported":49,"title":463,"abstract":464,"tags":465},[],[387,388,311],[387],{"code":376,"score":57,"supported":49,"title":467,"abstract":468,"tags":469},[],[372,392],[],{"code":172,"score":21,"supported":14,"title":471,"abstract":472,"tags":473},[],[],[455],{"code":71,"score":21,"supported":14,"title":475,"abstract":476,"tags":477},[],[],[30],[479,480],"赣江流域 SWAT-BiLSTM 径流模拟","赣江 泥沙 营养盐 模拟","赣江流域SWAT-BiLSTM径流模拟-4915","10.1016\u002Fj.jwpe.2026.111080",{"doi":482,"openalex_id":484,"authors":485,"venue":446,"cited_by_count":101,"oa_url":443,"card":506,"direction":108,"ingested_from":109},"W7221543472",[486,488,490,492,494,496,498,500,502,504],{"name":487,"orcid":9},"Xihua Wang",{"name":489,"orcid":9},"Xuming Ji",{"name":491,"orcid":9},"Y. Jun Xu",{"name":493,"orcid":9},"Boyang Mao",{"name":495,"orcid":9},"Shunqing Jia",{"name":497,"orcid":9},"Zejun Liu",{"name":499,"orcid":9},"Qinya Lv",{"name":501,"orcid":9},"Chengming Luo",{"name":503,"orcid":9},"Yan Dai",{"name":505,"orcid":9},"Yanxin Rong",{"tldr":507,"method":508,"finding":509,"direction":106,"opportunity":510},"提出SWAT-BiLSTM分层混合框架，高精度模拟赣江流域径流、泥沙与营养物动态。","SWAT模拟水文水质过程，BiLSTM残差校正，用2011-2022年多源数据验","混合模型精度全面优于SWAT，营养物R²提升9%-42%，PBIAS最大降67.4%。","可探索深度学习残差校正框架在农业面源污染关键源区识别与减排情景模拟中的推广。","2026-10-14T23:30:55.922882Z",{"id":513,"title":514,"url":515,"summary":516,"summary_zh":517,"content":9,"source_name":518,"source_url":515,"published_at":447,"category":12,"cover_url":9,"hotness":121,"is_selected":14,"score":519,"score_detail":520,"sources":522,"tags":524,"primary_topic":528,"secondary_topics":529,"classification":534,"search_phrases":547,"slug":550,"view_count":101,"doi":551,"paper":552,"created_at":572},4897,"Wild boar collision data and satellite computer vision refine habitat suitability mapping across France","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10344-026-02160-1","Abstract Wild boar (Sus scrofa) populations have expanded rapidly across Europe, leading to escalating human-wildlife conflicts (HWCs), notably wildlife-vehicle collisions (WVCs), increased agricultural damage and disease transmission. In continental France these issues are compounded by the species’ ecological adaptability exhibiting increasing overlaps with urbanization and transportation networks. In this study, we leverage time-series environmental data, computer vision and Species Distribution Modeling (SDM) to predict wild boar habitat suitability and investigate its spatiotemporal drivers. We use presence-only data from WVC reports on the national railway and road networks, along with publicly available GBIF observation collections, as complementary occurrence streams in our SDMs, while addressing inherent sampling biases of these datasets with tailored corrections. A key innovation of this study is the integration of national scale, annually updated high-resolution land cover predictors explicitly focused on wild boar resource preference. By fine-tuning a multitemporal Vision Transformer foundational AI model on multispectral satellite remote sensing imagery we capture subtle seasonal phenological differences. Our results highlight clear spatial, seasonal and annual variations in wild boar habitat suitability. The multitemporal SDM pipeline yields meaningful predictions when extrapolating to novel environmental scenarios such as climate extremes. Adding rail and road WVC records to GBIF-based SDMs retained high performance metrics while altering predicted spatial patterns and feature importance distribution. The methodological and ecological insights gained through this study provide additional knowledge for French transportation planning, agriculture and wildlife management. Identifying regions with high seasonal habitat suitability can inform targeted interventions. More broadly, our results demonstrate that advanced, data-driven methods are becoming indispensable for proactively and sustainably addressing HWCs in an increasingly anthropogenic world.","摘要 野猪（Sus scrofa）种群在欧洲迅速扩张，导致人兽冲突（HWCs）不断升级，尤其是野生动物车辆碰撞（WVCs）、农业损害加剧和疾病传播。在法国大陆，该物种的生态适应性使其与城市化和交通网络的重叠日益增加，进一步加剧了这些问题。本研究利用时间序列环境数据、计算机视觉和物种分布模型（SDM）预测野猪栖息地适宜性，并探究其时空驱动因素。我们使用国家铁路和公路网络中WVC报告的存在-only数据，以及公开可用的GBIF观测集合，作为SDM中的互补出现数据流，同时通过针对性校正处理这些数据集固有的采样偏差。本研究的一个关键创新是整合了国家尺度、年度更新的高分辨率土地覆盖预测因子，明确聚焦于野猪的资源偏好。通过在多光谱卫星遥感影像上微调多时相视觉Transformer基础AI模型，我们捕捉到了细微的季节物候差异。我们的结果突出了野猪栖息地适宜性在空间、季节和年际上的明显变化。多时相SDM流程在推断至气候极端等新型环境情景时产生了有意义的预测。将铁路和公路WVC记录添加到基于GBIF的SDM中，在保持高性能指标的同时改变了预测的空间格局和特征重要性分布。本研究获得的方法学和生态学见解为法国交通规划、农业和野生动物管理提供了额外知识。识别具有高季节性栖息地适宜性的区域可为针对性干预提供依据。更广泛地说，我们的结果表明，在日益受到人类影响的世界中，先进的数据驱动方法正成为前瞻性和可持续地应对人兽冲突不可或缺的手段。","European Journal of Wildlife Research",81,{"impact":17,"substance":18,"depth":17,"authority":449,"freshness":20,"relevant":21,"comment":521},"该研究将卫星遥感与计算机视觉用于野猪栖息地建模，方法新颖、数据规模大，对交通规划与农业野生动物管理有参考价值，但属法国区域研究，国内产业影响有限。",[523],{"name":518,"url":515},[370,31,525,526,527],"栖息地评估","野生动物管理","人兽冲突",{"code":35,"slug":36,"name":37,"path":38,"parent_code":39},[530],{"code":76,"slug":531,"name":532,"path":533,"parent_code":39},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":42,"locked":14,"version":43,"method":44,"evidence":535},{"candidates":536,"agricultural_evidence":49},[537,543],{"code":35,"score":48,"supported":49,"title":538,"abstract":540,"tags":542},[539],"computer vision",[541,539],"计算机视觉",[370],{"code":76,"score":121,"supported":49,"title":544,"abstract":545,"tags":546},[],[167,168,169],[31],[548,549],"法国 野猪 栖息地 遥感","野猪 车辆碰撞 卫星影像","法国野猪栖息地遥感-4897","10.1007\u002Fs10344-026-02160-1",{"doi":551,"openalex_id":553,"authors":554,"venue":518,"cited_by_count":101,"oa_url":515,"card":567,"direction":205,"ingested_from":109},"W7221702437",[555,558,560,563,565],{"name":556,"orcid":557},"Adrian Meyer","https:\u002F\u002Forcid.org\u002F0000-0002-8652-1477",{"name":559,"orcid":9},"Théo Reibel",{"name":561,"orcid":562},"Кevin Мorelle","https:\u002F\u002Forcid.org\u002F0000-0001-9041-6541",{"name":564,"orcid":9},"Mathias Kneubühler",{"name":566,"orcid":9},"Denis Jordan",{"tldr":568,"method":569,"finding":570,"direction":205,"opportunity":571},"融合野猪碰撞数据与卫星视觉模型，预测法国野猪栖息地适宜性及其时空驱动因素。","时间序列环境数据、Vision Transformer卫星影像、物种分布模型与G","栖息地适宜性呈明显时空变化，加入碰撞记录改变预测格局与特征重要性。","可将碰撞数据与遥感表型结合，拓展至其他野生动物-农业冲突的时空预警与决策。","2026-10-14T23:30:27.244673Z"]