[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3369":3,"related-3369":107},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":16,"sources":17,"tags":19,"primary_topic":25,"secondary_topics":31,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":75,"content":12,"score_detail":76,"classification":84},3369,"Understanding Land Use\u002FLand Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100916","Machine learning (ML)-based classification provides an effective approach for extracting land use\u002Fland cover (LULC) information from multi-temporal satellite observations. This study evaluates Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Forest (RF) for mapping LULC changes in Kullu district, Himachal Pradesh, India, using Landsat data from 1991 to 2022. SVM achieved the highest classification accuracy and was therefore used for subsequent LULC mapping. Six LULC classes were identified and their temporal changes were examined in relation to Landsat-derived land surface temperature (LST). The built-up area increased from 4.37% in 1991 to 20.40% in 2022, whereas vegetation declined from 34.41% to 16.23% and snow cover from 15.20% to 3.02%. The area-weighted mean LST was 4.10 °C in 1991, 10.03 °C in 2001, 8.43 °C in 2011, and 4.18 °C in 2022, representing a marginal net increase of 0.08 °C over the study period. Class-wise analysis showed that barren and agricultural lands generally recorded higher mean LST, while vegetation and waterbodies exhibited comparatively lower values. The relatively high snow-cover LST in 2022 may reflect reduced and fragmented seasonal snow cover and snowmelt conditions during image acquisition. Correlation analysis revealed an inverse relationship between LST and vegetation and snow-cover indicators, with R2 values reaching 0.66 for NDVI and generally exceeding 0.62 for NDSI. Overall, the findings demonstrate substantial LULC transformation and pronounced spatial differences in surface temperature, emphasizing the role of vegetation and snow cover in regulating thermal conditions and providing useful information for sustainable land management and spatial planning in the ecologically sensitive Himalayan environment.","基于机器学习的分类为从多时相卫星观测中提取土地利用\u002F土地覆盖（LULC）信息提供了一种有效途径。本研究利用1991年至2022年的Landsat数据，评估了最大似然分类（MLC）、支持向量机（SVM）和随机森林（RF）在印度喜马偕尔邦库鲁地区LULC变化制图中的表现。SVM取得了最高的分类精度，因此被用于后续的LULC制图。研究识别出六种LULC类别，并结合Landsat反演的地表温度（LST）分析了其时间变化。建设用地面积从1991年的4.37%增至2022年的20.40%，而植被从34.41%降至16.23%，积雪覆盖从15.20%降至3.02%。面积加权平均LST在1991年为4.10 °C，2001年为10.03 °C，2011年为8.43 °C，2022年为4.18 °C，研究期间净增幅度仅为0.08 °C。分类别分析表明，裸地和农用地通常具有较高的平均LST，而植被和水体的LST相对较低。2022年积雪覆盖区LST相对较高，可能反映了影像获取时期季节性积雪覆盖减少、破碎化及融雪条件的影响。相关分析揭示了LST与植被和积雪覆盖指标之间的反比关系，NDVI的R²达到0.66，NDSI的R²普遍超过0.62。总体而言，研究结果揭示了显著的LULC转变和地表温度的明显空间差异，强调了植被和积雪覆盖在调节热力条件中的作用，并为生态敏感的喜马拉雅地区的可持续土地管理和空间规划提供了有用信息。","Atmosphere","2026-09-23T00:00:00Z","论文",null,10,false,true,67,[18],{"name":9,"url":6},[20,21,22,23,24],"机器学习","遥感","土地利用","地表温度","喜马拉雅",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],[33,34],"Kullu 土地利用 地表温度","Landsat 机器学习 土地覆盖","Kullu土地利用地表温度-3369",3,"10.3390\u002Fatmos17100916",{"doi":37,"openalex_id":39,"authors":40,"venue":9,"cited_by_count":66,"oa_url":6,"card":67,"direction":73,"ingested_from":74},"W7214105064",[41,44,46,49,52,55,58,60,63],{"name":42,"orcid":43},"Sanjeev Jha","https:\u002F\u002Forcid.org\u002F0000-0002-6851-7722",{"name":45,"orcid":12},"Nirbhav",{"name":47,"orcid":48},"Atul Saini","https:\u002F\u002Forcid.org\u002F0000-0001-9840-2486",{"name":50,"orcid":51},"Toushif Jaman","https:\u002F\u002Forcid.org\u002F0000-0001-6225-5648",{"name":53,"orcid":54},"Harish Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-4738-1248",{"name":56,"orcid":57},"Pradeep Kumar Jha","https:\u002F\u002Forcid.org\u002F0000-0002-5938-1747",{"name":59,"orcid":12},"Fahdah Falah Ben Hasher",{"name":61,"orcid":62},"Abinash SİLWAL","https:\u002F\u002Forcid.org\u002F0000-0002-2285-4643",{"name":64,"orcid":65},"Mohamed Zhran","https:\u002F\u002Forcid.org\u002F0000-0002-1112-387X",0,{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"用机器学习与遥感分析印度库鲁区1991-2022年土地利用变化及其对地表温度的影响。","Landsat时序数据，对比MLC、SVM、随机森林分类，结合NDVI\u002FNDSI","建设用地从4.37%增至20.40%，植被与积雪大幅减少，LST净增0.08°C，植被和积雪与LST","农业遥感与作物表型","可引入深度学习与多源遥感，在喜马拉雅生态敏感区开展LULC-LST耦合模拟与情景预测。","农业人工智能与决策模型","openalex","2026-09-24T23:30:34.225644Z",{"impact":77,"substance":78,"depth":79,"authority":80,"freshness":81,"relevant":82,"comment":83},8,20,17,13,9,1,"基于Landsat与机器学习揭示喜马拉雅地区土地利用与地表温度长期演变，数据翔实但属区域案例研究，公共影响有限。",{"status":85,"locked":14,"version":86,"method":87,"evidence":88},"classified","2026-10-22.2","rules",{"candidates":89,"agricultural_evidence":15},[90,96,102],{"code":26,"score":91,"supported":15,"title":92,"abstract":94,"tags":95},15,[93],"machine learning",[20,93],[20],{"code":97,"score":98,"supported":14,"title":99,"abstract":100,"tags":101},"I3",2,[],[],[23,21],{"code":103,"score":82,"supported":14,"title":104,"abstract":105,"tags":106},"H6",[],[],[22],{"total":108,"page":82,"page_size":108,"items":109},6,[110,144,184,226,261,334],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"source_name":116,"source_url":113,"published_at":117,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":118,"sources":119,"tags":121,"primary_topic":124,"secondary_topics":125,"search_phrases":126,"slug":129,"view_count":98,"doi":130,"paper":131,"created_at":143},3196,"Geospatial and machine learning techniques for spatiotemporal analyses of urban dynamics in the Coimbatore city, South India","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fjeelm.2026.28264","Rapid urbanization has drastically changed the land use and environmental conditions in Indian cities and need to be monitored continuously for sustainable urban planning. The study used Landsat satellite images of the years 2001, 2013 and 2023 to examine the spatiotemporal urban dynamics of the Coimbatore city of South India. The impacts of urban sprawl on the environment were evaluated from Land Use\u002F Land Cover (LULC), Land Surface Temperature (LST) and spectral indices (NDVI, NDWI and NDBI). The built-up land increased by 1.21% (2001–2013) and 1.67% (2013–2023) and agricultural land decreased by 1.62% (2013–2023). The LULC classification had an Overall Accuracy of 95.76% with a Kappa coefficient of 0.95. The Kappa coefficient of 0.96 indicates that the ANN-CA model has high predictive reliability in predicting the LULC scenario in 2031. The results show that the continued expansion of cities leads to an increase in land surface temperature and a decrease in vegetation cover. This has major implications for sustainable urban development and contributes to SDG 11 and SDG 13.","快速城市化极大地改变了印度城市的土地利用和环境状况，需要持续监测以支持可持续城市规划。本研究利用2001年、2013年和2023年的Landsat卫星影像，考察了印度南部哥印拜陀市的时空城市动态。研究从土地利用\u002F土地覆盖（LULC）、地表温度（LST）和光谱指数（NDVI、NDWI和NDBI）方面评估了城市蔓延对环境的影响。建设用地在2001—2013年间增加了1.21%，在2013—2023年间增加了1.67%；农业用地在2013—2023年间减少了1.62%。LULC分类的总体精度为95.76%，Kappa系数为0.95。Kappa系数0.96表明，ANN-CA模型在预测2031年LULC情景方面具有较高的预测可靠性。结果表明，城市持续扩张导致地表温度升高和植被覆盖减少。这对可持续城市发展具有重要影响，并有助于实现可持续发展目标11和可持续发展目标13。","Journal of Environmental Engineering and Landscape Management","2026-09-21T00:00:00Z",62,[120],{"name":116,"url":113},[122,21,22,23,123],"可持续发展","城市扩张",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],[127,128],"Coimbatore 城市扩张 遥感","LULC LST NDVI 印度城市","Coimbatore城市扩张遥感-3196","10.3846\u002Fjeelm.2026.28264",{"doi":130,"openalex_id":132,"authors":133,"venue":116,"cited_by_count":66,"oa_url":113,"card":138,"direction":73,"ingested_from":74},"W7213862636",[134,136],{"name":135,"orcid":12},"Nagamani Singamuthu",{"name":137,"orcid":12},"Elangovan Krishnan",{"tldr":139,"method":140,"finding":141,"direction":71,"opportunity":142},"基于Landsat影像与机器学习分析印度哥印拜陀市2001-2023年城市扩张及其环境效应。","Landsat影像、LULC分类、NDVI\u002FNDWI\u002FNDBI指数、ANN-CA","建设用地增加、农地减少，城市扩张导致地表温度上升、植被覆盖下降。","可结合多源遥感与深度学习提升城市扩张预测精度，并探究其对周边农业用地的长期影响。","2026-09-22T23:30:43.496849Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"source_name":150,"source_url":147,"published_at":151,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":152,"sources":153,"tags":155,"primary_topic":159,"secondary_topics":160,"search_phrases":161,"slug":164,"view_count":66,"doi":165,"paper":166,"created_at":183},5609,"Structural Reorganization of Water Use Efficiency Drivers in the Hanjiang River Basin Under the South-to-North Water Diversion: Spatiotemporal Deep Learning and Pixel-Level Attribution","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fw18202617","Interbasin water transfer reshapes water use efficiency (WUE) by altering hydrology and land use, yet post-transfer WUE changes and driver reorganization remain poorly quantified. Focusing on the Hanjiang River Basin (South-to-North Water Diversion source), we employed a convolutional gated recurrent unit (ConvGRU) deep learning model with Integrated Gradients to quantify pixel-scale spatiotemporal contributions of nine drivers to WUE over 2000–2024 using meteorological and multi-source remote sensing data. Results show (1) Basin-wide WUE increased over 2000–2024, but post-diversion growth dropped sharply (0.0184 to 0.0045 yr−1), with river-channel declines driving this deceleration. (2) Temperature and vegetation land-cover type were the strongest suppressive drivers, while cropland and vapor pressure were the strongest promotive drivers; after diversion, kNDVI exhibited the largest attribution increase (+0.14), and the center of gravity of driving forces is shifting from climatic factors toward vegetation and land-use factors. (3) Across the basin, 42.26% of areas underwent a switch in dominant driver, with the most intense reorganization occurring in the upper reaches; the dominant area of cropland rose from 26.28% to 29.24%, kNDVI nearly doubled from 9.09% to 16.64%, and its expansion simultaneously absorbed dominance from temperature, radiation, cropland, and vegetation land-cover type—a “multi-source convergence” pattern of reorganization. Sensitivity analysis across multiple baseline schemes confirmed the robustness of the attribution conclusions. These findings inform ecological and agricultural water management in the Hanjiang Basin under the diversion.","跨流域调水通过改变水文过程和土地利用重塑水资源利用效率（WUE），但调水后的WUE变化及驱动因子重组仍缺乏系统量化。本研究以汉江流域（南水北调水源区）为对象，采用卷积门控循环单元（ConvGRU）深度学习模型结合积分梯度方法，基于气象数据与多源遥感数据，量化了2000—2024年九个驱动因子对WUE的像元尺度时空贡献。结果表明：（1）2000—2024年全流域WUE呈上升趋势，但调水后增速大幅下降（由0.0184 yr⁻¹降至0.0045 yr⁻¹），河道区域WUE下降是增速减缓的主要驱动力。（2）气温和植被土地覆盖类型是最强的抑制性驱动因子，而耕地和水汽压是最强的促进性驱动因子；调水后，kNDVI的归因贡献增幅最大（+0.14），驱动力重心正由气候因子向植被和土地利用因子转移。（3）全流域42.26%的区域主导驱动因子发生转换，其中上游地区重组最为剧烈；耕地主导区占比由26.28%升至29.24%，kNDVI主导区占比由9.09%增至16.64%，近乎翻倍，其扩张同时吸收了气温、辐射、耕地和植被土地覆盖类型的主导地位，呈现“多源汇聚”的重组格局。多基线方案敏感性分析验证了归因结论的稳健性。研究结果可为调水背景下汉江流域生态与农业水资源管理提供科学依据。","Water","2026-10-21T00:00:00Z",75,[154],{"name":150,"url":147},[156,21,157,22,158],"深度学习","水资源管理","灌溉用水",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],[162,163],"汉江流域 南水北调 水资源利用效率","ConvGRU 遥感 归因分析","汉江流域南水北调水资源利用效率-5609","10.3390\u002Fw18202617",{"doi":165,"openalex_id":167,"authors":168,"venue":150,"cited_by_count":66,"oa_url":147,"card":178,"direction":71,"ingested_from":74},"W7223172901",[169,172,174,176],{"name":170,"orcid":171},"Xuepeng Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0224-7907",{"name":173,"orcid":12},"Haoxuan Jin",{"name":175,"orcid":12},"Zhe Wang",{"name":177,"orcid":12},"Yingying Zhang",{"tldr":179,"method":180,"finding":181,"direction":71,"opportunity":182},"用ConvGRU与积分梯度量化南水北调后汉江流域WUE驱动因子的像素级重组。","ConvGRU深度学习结合积分梯度，2000–2024气象与多源遥感数据归因。","调水后WUE增速骤降，42.26%区域主导驱动因子切换，驱动力向植被与土地利用转移。","可探究调水工程下耕地与植被因子“多源汇聚”机制，并耦合灌溉管理数据做农业WUE精细归因。","2026-10-22T23:30:40.833465Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":189,"source_name":190,"source_url":187,"published_at":191,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":66,"sources":192,"tags":194,"primary_topic":196,"secondary_topics":197,"search_phrases":203,"slug":206,"view_count":66,"doi":207,"paper":208,"created_at":225},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",[193],{"name":190,"url":187},[20,195,22,123],"遥感监测",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[198],{"code":199,"slug":200,"name":201,"path":202,"parent_code":30},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[204,205],"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":207,"openalex_id":209,"authors":210,"venue":190,"cited_by_count":66,"oa_url":187,"card":220,"direction":71,"ingested_from":74},"W7223022155",[211,214,216,218],{"name":212,"orcid":213},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":215,"orcid":12},"Goanar Jal Dup",{"name":217,"orcid":12},"Weyesa Merga Chemeda",{"name":219,"orcid":12},"Shankar Karuppannan",{"tldr":221,"method":222,"finding":223,"direction":71,"opportunity":224},"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":227,"title":228,"url":229,"summary":230,"summary_zh":231,"source_name":232,"source_url":233,"published_at":234,"category":11,"cover_url":12,"hotness":235,"is_selected":14,"embedding_ready":15,"score":236,"sources":237,"tags":241,"primary_topic":243,"secondary_topics":244,"search_phrases":245,"slug":248,"view_count":66,"doi":249,"paper":250,"created_at":260},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,[238,239],{"name":232,"url":233},{"name":232,"url":240},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[20,21,157,242],"地下水监测",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],[246,247],"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":249,"openalex_id":251,"authors":252,"venue":232,"cited_by_count":66,"oa_url":233,"card":255,"direction":71,"ingested_from":74},"W7222805722",[253],{"name":254,"orcid":12},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":256,"method":257,"finding":258,"direction":71,"opportunity":259},"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",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"source_name":267,"source_url":264,"published_at":268,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":269,"sources":270,"tags":272,"primary_topic":276,"secondary_topics":282,"search_phrases":288,"slug":291,"view_count":66,"doi":292,"paper":293,"created_at":333},5178,"Spatial Modeling of Topsoil Total Carbon and Texture from Spectral Characteristics of Bare Soil Surface Images of Landsat 5\u002F8 Using Machine Learning","https:\u002F\u002Fdoi.org\u002F10.1134\u002Fs1064229326601228","— This study examines the possibility for detecting bare soil surface (BSS), using remote sensing data and for applying this approach to model texture and total soil carbon in the topsoil. The study area comprises agricultural fields within the North Caucasus Federal Agricultural Research Center in the northeastern spurs of the Stavropol Upland. Landsat 5\u002F8 imagery and spectral indices (NDVI, NBR2, and BSI) were used to classify BSS. Optimal discrimination of BSS from green and non-photosynthetic vegetation was achieved, using a BSI threshold of >0.18 (F1-score = 0.98). Spatial modeling of topsoil properties was conducted by comparing five regression models (Linear Regression, K-Nearest Neighbors, Random Forest, Extreme Gradient Boosting, and Cubist). A linear regression model demonstrated the highest performance; key predictors included the mean and standard deviation of SWIR2. The model exhibits strong predictive capability for sand, silt, clay, and total carbon (adjusted R 2 = 0.82, 0.75, 0.78, and 0.74, respectively). The topsoil is characterized by the range of clay content from 5 to 30%, of silt content from 15 to 60%, of sand content from 20 to 80%, and of total carbon from 0.5 to 3%. Predictive maps of texture and total carbon derived from BSS reveal considerable local heterogeneity, with soils consisting of Chernozems and Phaeozems, influenced by parent rock variability. The modeling results highlight the high predictive power of the BSS spectral characteristics, even when used as a single predictor for digital soil mapping.","本研究探讨了利用遥感数据检测裸露土壤表面（BSS）的可能性，并将该方法应用于表层土壤质地和总碳的建模。研究区包括位于斯塔夫罗波尔高地东北支脉的北高加索联邦农业研究中心内的农田。利用Landsat 5\u002F8影像和光谱指数（NDVI、NBR2和BSI）对BSS进行分类。当BSI阈值>0.18时，实现了BSS与绿色植被及非光合植被的最优区分（F1分数=0.98）。通过比较五种回归模型（线性回归、K近邻、随机森林、极端梯度提升和Cubist）对表层土壤属性进行了空间建模。线性回归模型表现出最高的性能；关键预测变量包括SWIR2的均值和标准差。该模型对砂粒、粉粒、黏粒和总碳表现出较强的预测能力（调整R²分别为0.82、0.75、0.78和0.74）。表层土壤的黏粒含量范围为5%至30%，粉粒含量为15%至60%，砂粒含量为20%至80%，总碳含量为0.5%至3%。基于BSS生成的质地和总碳预测图揭示了显著的局部异质性，土壤由黑钙土和暗色始成土组成，受母岩变异性的影响。建模结果凸显了BSS光谱特征的高预测能力，即使仅作为数字土壤制图的单一预测变量使用亦然。","Eurasian Soil Science","2026-10-15T00:00:00Z",69,[271],{"name":267,"url":264},[20,21,273,274,275],"土壤碳","Landsat","数字土壤制图",{"code":277,"slug":278,"name":279,"path":280,"parent_code":281},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[283,284],{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},{"code":97,"slug":285,"name":286,"path":287,"parent_code":30},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[289,290],"Landsat 裸土 土壤碳","北高加索 土壤质地 遥感","Landsat裸土土壤碳-5178","10.1134\u002Fs1064229326601228",{"doi":292,"openalex_id":294,"authors":295,"venue":267,"cited_by_count":66,"oa_url":12,"card":328,"direction":71,"ingested_from":74},"W7222107492",[296,299,302,305,308,311,314,317,319,322,325],{"name":297,"orcid":298},"P. A. Braulov","https:\u002F\u002Forcid.org\u002F0000-0001-9919-7402",{"name":300,"orcid":301},"Pavel M. Shilov","https:\u002F\u002Forcid.org\u002F0000-0003-4113-6396",{"name":303,"orcid":304},"R. G. Gadzhiumarov","https:\u002F\u002Forcid.org\u002F0000-0002-4220-623X",{"name":306,"orcid":307},"V. K. Dridiger","https:\u002F\u002Forcid.org\u002F0000-0002-0510-2220",{"name":309,"orcid":310},"E. V. Bogdan","https:\u002F\u002Forcid.org\u002F0009-0006-4781-5786",{"name":312,"orcid":313},"V. O. Megmerova","https:\u002F\u002Forcid.org\u002F0000-0001-8655-6437",{"name":315,"orcid":316},"Petr Tsymbarovich","https:\u002F\u002Forcid.org\u002F0000-0001-8591-2612",{"name":318,"orcid":12},"A. E. Shatunov",{"name":320,"orcid":321},"Pavel P. Fil","https:\u002F\u002Forcid.org\u002F0000-0002-9851-5381",{"name":323,"orcid":324},"D. E. Mitichkin","https:\u002F\u002Forcid.org\u002F0009-0006-2397-060X",{"name":326,"orcid":327},"Dmitriy S. Fomin","https:\u002F\u002Forcid.org\u002F0000-0003-3733-0284",{"tldr":329,"method":330,"finding":331,"direction":71,"opportunity":332},"用Landsat 5\u002F8裸土光谱指数识别裸土并机器学习建模表层土壤质地与全碳。","Landsat 5\u002F8影像、NDVI\u002FNBR2\u002FBSI指数、五种回归模型对比。","BSI阈值0.18识别裸土F1达0.98，线性回归最优，SWIR2均值与标准差为关键预测因子。","可探索多时相裸土合成与深度学习模型，提升数字土壤制图精度与跨区域迁移能力。","2026-10-17T23:30:31.485949Z",{"id":335,"title":336,"url":337,"summary":338,"summary_zh":339,"source_name":340,"source_url":337,"published_at":341,"category":11,"cover_url":12,"hotness":13,"is_selected":14,"embedding_ready":15,"score":66,"sources":342,"tags":344,"primary_topic":347,"secondary_topics":348,"search_phrases":349,"slug":352,"view_count":82,"doi":353,"paper":354,"created_at":375},4317,"Machine-learning modelling of regional landslide susceptibility in eThekwini, South Africa","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-73316-x","The occurrence of landslides, particularly within urban landscapes, has gained significant research interest because of their devastating impacts on infrastructure, resulting in casualties, displacement, and economic losses. This study aimed to examine the probability of landslide events and landslide-prone areas within the eThekwini Metropolitan Municipality in KwaZulu-Natal Province, South Africa. To achieve this, robust machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), combined with geospatial tools and key landslide-triggering factors, were considered in the analysis and characterised based on susceptibility indexes ranging from very low to very high based on various carefully chosen thresholds. The results indicate that land use, elevation, NDVI, and lithology are important landslide-influencing factors. In contrast, land use parameters, such as cultivated land and built-up areas, were associated with high susceptibility zones. Furthermore, RF yielded slightly higher overall accuracy scores than SVM, with accuracy scores of 99.45% and 98.63%, respectively. The SVM susceptibility output was further analysed based on the spatial distribution of formal and informal housing types. The results confirmed that only a small percentage of each housing type fell within the high-susceptibility zones, calculated as 1.33% and 2.41% for formal and informal housing, respectively. Although SVM is one of the strongest models, RF shows higher accuracy in landslide susceptibility modelling. Furthermore, its slightly higher recall score compared to its precision score is deemed most applicable for identifying landslide-prone areas. The study suggested that human-engineered land-use activities, such as road and property development, contribute significantly to increased landslide susceptibility within the eThekwini area.","滑坡的发生，尤其是在城市景观中，因其对基础设施的破坏性影响，导致人员伤亡、流离失所和经济损失，已引起广泛的研究关注。本研究旨在考察南非夸祖鲁-纳塔尔省eThekwini大都市自治市范围内滑坡事件的发生概率及滑坡易发区域。为实现这一目标，本研究在分析中采用了稳健的机器学习模型，包括随机森林（Random Forest, RF）和支持向量机（Support Vector Machines, SVM），并结合地理空间工具和关键滑坡触发因子，基于多种精心选取的阈值，按照从极低到极高的易发性指数进行了表征。结果表明，土地利用、海拔、归一化植被指数（NDVI）和岩性是重要的滑坡影响因素。相比之下，耕地和建成区等土地利用参数与高易发性区域相关。此外，RF的总体精度得分略高于SVM，分别为99.45%和98.63%。研究进一步基于正式住房和非正式住房类型的空间分布对SVM易发性输出进行了分析。结果证实，每种住房类型仅有一小部分位于高易发性区域内，正式住房和非正式住房分别为1.33%和2.41%。尽管SVM是最强的模型之一，但RF在滑坡易发性建模中表现出更高的精度。此外，其召回率得分略高于精确率得分，被认为最适合用于识别滑坡易发区域。研究提示，人为工程土地利用活动，如道路和房地产开发，对eThekwini地区滑坡易发性的增加具有显著贡献。","Scientific Reports","2026-10-04T00:00:00Z",[343],{"name":340,"url":337},[20,21,345,346],"滑坡易发性","城市地质",{"code":26,"slug":27,"name":28,"path":29,"parent_code":30},[],[350,351],"eThekwini 滑坡 易发性","Random Forest 滑坡 建模","eThekwini滑坡易发性-4317","10.1038\u002Fs41598-026-73316-x",{"doi":353,"openalex_id":355,"authors":356,"venue":340,"cited_by_count":66,"oa_url":369,"card":370,"direction":71,"ingested_from":74},"W7219918606",[357,360,363,366],{"name":358,"orcid":359},"Cher Petersen","https:\u002F\u002Forcid.org\u002F0000-0002-0434-6928",{"name":361,"orcid":362},"Paidamwoyo Mhangara","https:\u002F\u002Forcid.org\u002F0000-0002-0594-6626",{"name":364,"orcid":365},"Laven Naidoo","https:\u002F\u002Forcid.org\u002F0000-0002-7091-0566",{"name":367,"orcid":368},"Eskinder Gidey","https:\u002F\u002Forcid.org\u002F0000-0001-6953-5562","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-73316-x_reference.pdf",{"tldr":371,"method":372,"finding":373,"direction":71,"opportunity":374},"用随机森林与支持向量机评估南非eThekwini地区滑坡易发性并分析住房风险。","随机森林、支持向量机结合地理空间工具与NDVI、岩性等触发因子。","土地利用、高程、NDVI和岩性最关键；RF精度99.45%略高于SVM，非正式住房更易受灾。","可探索将滑坡易发性模型与农业土地利用规划结合，评估农田与乡村道路的灾害风险。","2026-10-06T23:30:21.860018Z"]