[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4308":3,"related-4308":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":79},4308,"GIS, Remote Sensing and Machine Learning Applications for Forest Fire Mapping, Susceptibility Assessment and Risk Analysis: A Systematic Review","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11233692\u002Fv1","GIS, Remote Sensing and Machine Learning Applications for Forest Fire Mapping, Susceptibility Assessment and Risk Analysis: A Systematic Review。Research Square",null,"Research Square","2026-10-05T00:00:00Z","论文",10,false,63,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,8,9,1,"系统综述梳理遥感与机器学习在森林火灾制图与风险评估中的应用，对林业信息化与防灾有参考价值，但属综述类论文、来源为预印本平台，权威性一般。",[24],{"name":9,"url":6},[26,27,28,29,30],"机器学习","遥感","GIS","森林防火","风险评估",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":13,"reason":61},[38,45,51,56],{"code":39,"score":16,"supported":40,"title":41,"abstract":43,"tags":44},"I2",true,[42],"machine learning",[42],[26],{"code":46,"score":16,"supported":40,"title":47,"abstract":49,"tags":50},"I3",[48],"remote sensing",[48],[27],{"code":52,"score":16,"supported":40,"title":53,"abstract":54,"tags":55},"I4",[28],[28],[28],{"code":57,"score":21,"supported":13,"title":58,"abstract":59,"tags":60},"H7",[],[],[29],"农业相关性需要复核",[63,64],"森林火灾 遥感 机器学习","GIS 森林火险 评估","森林火灾遥感机器学习-4308",2,"10.21203\u002Frs.3.rs-11233692\u002Fv1",{"doi":67,"openalex_id":69,"authors":70,"venue":9,"cited_by_count":75,"oa_url":76,"card":8,"direction":77,"ingested_from":78},"W7220368159",[71,73],{"name":72,"orcid":8},"Lucius Malalu",{"name":74,"orcid":8},"Henry Chinthuli",0,"https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11233692\u002Flatest.pdf","农业遥感与作物表型","openalex","2026-10-06T23:30:19.935677Z",{"total":81,"page":21,"page_size":81,"items":82},6,[83,151,228,294,365,437],{"id":84,"title":85,"url":86,"summary":87,"summary_zh":88,"content":8,"source_name":89,"source_url":86,"published_at":90,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":92,"sources":96,"tags":98,"primary_topic":8,"secondary_topics":100,"classification":101,"search_phrases":118,"slug":121,"view_count":75,"doi":122,"paper":123,"created_at":150},4439,"Evaluation of risk scenarios in Ecuador using Visible Infrared Imaging Radiometer Suite remote sensing, ERA5-Land climate data, and XGBoost","https:\u002F\u002Fdoi.org\u002F10.12912\u002F27197050\u002F240190","The present study aims to develop and optimize a spatial risk scenarios model for forest fires in Ecuador through the integration of machine learning and satellite remote sensing.To this end, 103,224 historical records of thermal anomalies captured by the VIIRS sensor were processed, along with five continuous meteorological variables from the ERA5-Land global reanalysis, spatial coordinates (latitude and longitude), and temporal indicators (month of the year), spanning the period between 2015 and 2025.The methodology was based on the construction of a 5-kilometer resolution spatial grid coupled with the XGBoost classification algorithm, designed to efficiently manage class imbalance in the face of extreme climate events.The computational system achieved an accuracy level of 81% on the test set, validating its robustness with a true positive rate of 0.86 and an area under the ROC curve of 0.8382.Furthermore, the feature importance analysis determined that precipitation deficits and seasonal timing act as the primary drivers of ignition risk, exerting a significantly greater influence than surface temperature, concluding that this scalable and open-access tool allows for high-precision mapping of vulnerable areas in the Andean and coastal regions through scenario-based experiments, aiming to optimize the distribution of state preventive resources against the constant threat of global warming.","本研究旨在通过整合机器学习与卫星遥感技术，开发并优化厄瓜多尔森林火灾的空间风险情景模型。为此，研究处理了VIIRS传感器捕获的103 224条历史热异常记录，以及来自ERA5-Land全球再分析数据的五个连续气象变量、空间坐标（纬度和经度）和时间指标（月份），时间跨度为2015年至2025年。方法基于构建5公里分辨率空间网格并结合XGBoost分类算法，旨在高效应对极端气候事件下的类别不平衡问题。该计算系统在测试集上达到了81%的准确率，以0.86的真阳性率和0.8382的ROC曲线下面积验证了其稳健性。此外，特征重要性分析确定，降水亏缺和季节时序是点火风险的主要驱动因素，其影响显著大于地表温度。研究结论认为，这一可扩展且开放获取的工具通过情景实验实现了对安第斯和沿海地区脆弱区域的高精度制图，旨在优化国家预防资源在全球变暖持续威胁下的配置。","Ecological Engineering & Environmental Technology","2026-10-06T00:00:00Z",66,{"impact":19,"substance":93,"depth":94,"authority":16,"freshness":20,"relevant":21,"comment":95},20,17,"方法扎实、数据规模大的遥感+机器学习火灾风险建模论文，但属厄瓜多尔区域研究，对国内三农信息化仅有方法借鉴价值。",[97],{"name":89,"url":86},[26,27,29,30,99],"厄瓜多尔",[],{"status":33,"locked":13,"version":34,"method":35,"evidence":102},{"candidates":103,"agricultural_evidence":13,"reason":61},[104,109,114],{"code":46,"score":105,"supported":40,"title":106,"abstract":107,"tags":108},15,[48],[27,48],[27],{"code":39,"score":110,"supported":40,"title":111,"abstract":112,"tags":113},7,[],[26,42],[26],{"code":57,"score":21,"supported":13,"title":115,"abstract":116,"tags":117},[],[],[29],[119,120],"厄瓜多尔 森林火灾 风险","厄瓜多尔 机器学习 森林防火 风险评估","厄瓜多尔森林火灾风险-4439","10.12912\u002F27197050\u002F240190",{"doi":122,"openalex_id":124,"authors":125,"venue":89,"cited_by_count":75,"oa_url":144,"card":145,"direction":77,"ingested_from":78},"W7220666178",[126,129,132,135,138,141],{"name":127,"orcid":128},"César José Avila Martínez","https:\u002F\u002Forcid.org\u002F0009-0009-3253-8300",{"name":130,"orcid":131},"Byrone Antonio Almeida Salazar","https:\u002F\u002Forcid.org\u002F0000-0002-4412-1125",{"name":133,"orcid":134},"Byron Gustavo Almeida Gavino","https:\u002F\u002Forcid.org\u002F0009-0005-7811-2226",{"name":136,"orcid":137},"Christian Manuel Quishpe Villa","https:\u002F\u002Forcid.org\u002F0009-0005-3710-860X",{"name":139,"orcid":140},"Fabián Alexander Molina Peralvo","https:\u002F\u002Forcid.org\u002F0009-0007-6296-0751",{"name":142,"orcid":143},"Alex Jesús López Campuzano","https:\u002F\u002Forcid.org\u002F0009-0001-0916-551X","https:\u002F\u002Fwww.ecoeet.com\u002Fpdf-240190-149057?filename=Evaluation-of-risk-scenar.pdf",{"tldr":146,"method":147,"finding":148,"direction":77,"opportunity":149},"结合VIIRS热异常、ERA5-Land气象数据与XGBoost，构建厄瓜多尔5公里分辨率森林火灾风","VIIRS热异常与ERA5-Land气象变量，5公里网格加XGBoost分类，处","测试集精度81%，AUC 0.8382；降水亏缺与季节时相比地表温度更主导火险。","可将该遥感+机器学习火险框架迁移至中国农林交错带，融合植被指数与人类活动因子提升预警精度。","2026-10-08T23:30:30.817265Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":8,"source_name":157,"source_url":154,"published_at":158,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":159,"score_detail":160,"sources":163,"tags":165,"primary_topic":169,"secondary_topics":174,"classification":175,"search_phrases":208,"slug":211,"view_count":75,"doi":212,"paper":213,"created_at":227},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":19,"substance":17,"depth":161,"authority":16,"freshness":20,"relevant":21,"comment":162},14,"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[164],{"name":157,"url":154},[27,166,167,28,168],"土地利用","土壤侵蚀","水土保持",{"code":52,"slug":170,"name":171,"path":172,"parent_code":173},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":176,"locked":13,"version":34,"method":35,"evidence":177},"classified",{"candidates":178,"agricultural_evidence":40},[179,185,189,196,203],{"code":52,"score":12,"supported":40,"title":180,"abstract":181,"tags":184},[],[182,28,183],"地理信息","geographic information",[28],{"code":46,"score":110,"supported":40,"title":186,"abstract":187,"tags":188},[],[27,48],[27],{"code":190,"score":81,"supported":40,"title":191,"abstract":192,"tags":195},"H2",[],[193,194],"土壤","soil",[],{"code":197,"score":198,"supported":40,"title":199,"abstract":200,"tags":202},"H3",3,[],[201],"灌溉",[],{"code":204,"score":66,"supported":13,"title":205,"abstract":206,"tags":207},"H6",[],[],[166,168],[209,210],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":212,"openalex_id":214,"authors":215,"venue":157,"cited_by_count":75,"oa_url":8,"card":221,"direction":77,"ingested_from":78},"W7223117733",[216,218],{"name":217,"orcid":8},"MERİÇ KURT",{"name":219,"orcid":220},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":222,"method":223,"finding":224,"direction":225,"opportunity":226},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":8,"source_name":234,"source_url":231,"published_at":235,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":245,"secondary_topics":249,"classification":250,"search_phrases":273,"slug":276,"view_count":75,"doi":277,"paper":278,"created_at":293},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z",70,{"impact":16,"substance":93,"depth":94,"authority":16,"freshness":20,"relevant":21,"comment":238},"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[240],{"name":234,"url":231},[242,27,28,243,244],"农业人工智能","野生动物保护","人兽冲突",{"code":173,"slug":246,"name":247,"path":248,"parent_code":8},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":251,"locked":13,"version":34,"method":35,"evidence":252},"partial",{"candidates":253,"agricultural_evidence":40,"reason":272},[254,259,263,268],{"code":39,"score":12,"supported":40,"title":255,"abstract":256,"tags":258},[],[26,42,257],"decision support",[242],{"code":52,"score":12,"supported":40,"title":260,"abstract":261,"tags":262},[],[182,28,183],[28],{"code":46,"score":264,"supported":40,"title":265,"abstract":266,"tags":267},4,[],[27],[27],{"code":204,"score":21,"supported":13,"title":269,"abstract":270,"tags":271},[],[],[243],"一级领域明确，二级方向需要细分",[274,275],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":277,"openalex_id":279,"authors":280,"venue":234,"cited_by_count":75,"oa_url":231,"card":287,"direction":292,"ingested_from":78},"W7222999966",[281,283,285],{"name":282,"orcid":8},"Bhutia Dechen Doma",{"name":284,"orcid":8},"Swarup Das",{"name":286,"orcid":8},"Mandal Rakesh Kumar",{"tldr":288,"method":289,"finding":290,"direction":77,"opportunity":291},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","农业人工智能与决策模型","2026-10-21T23:30:43.936453Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":8,"source_name":300,"source_url":297,"published_at":301,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":302,"score_detail":303,"sources":305,"tags":307,"primary_topic":311,"secondary_topics":312,"classification":313,"search_phrases":348,"slug":351,"view_count":75,"doi":352,"paper":353,"created_at":364},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":19,"substance":18,"depth":105,"authority":198,"freshness":19,"relevant":21,"comment":304},"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[306],{"name":300,"url":297},[308,27,28,309,310],"决策支持系统","水稻种植","土地评价",{"code":173,"slug":246,"name":247,"path":248,"parent_code":8},[],{"status":251,"locked":13,"version":34,"method":35,"evidence":314},{"candidates":315,"agricultural_evidence":40,"reason":272},[316,320,324,333,340,344],{"code":39,"score":16,"supported":40,"title":317,"abstract":318,"tags":319},[257],[257],[308],{"code":52,"score":16,"supported":40,"title":321,"abstract":322,"tags":323},[28],[28],[28],{"code":325,"score":12,"supported":40,"title":326,"abstract":327,"tags":332},"A1",[],[328,329,330,331],"水稻","稻田","rice","paddy",[309],{"code":334,"score":81,"supported":40,"title":335,"abstract":336,"tags":339},"A8",[],[337,338],"粮食安全","food security",[],{"code":190,"score":81,"supported":40,"title":341,"abstract":342,"tags":343},[],[193,194],[],{"code":46,"score":21,"supported":13,"title":345,"abstract":346,"tags":347},[],[],[27],[349,350],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":352,"openalex_id":354,"authors":355,"venue":300,"cited_by_count":75,"oa_url":297,"card":359,"direction":292,"ingested_from":78},"W7222963628",[356],{"name":357,"orcid":358},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":360,"method":361,"finding":362,"direction":77,"opportunity":363},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z",{"id":366,"title":367,"url":368,"summary":369,"summary_zh":370,"content":8,"source_name":371,"source_url":372,"published_at":373,"category":11,"cover_url":8,"hotness":374,"is_selected":13,"score":375,"score_detail":376,"sources":378,"tags":382,"primary_topic":385,"secondary_topics":389,"classification":390,"search_phrases":421,"slug":424,"view_count":75,"doi":425,"paper":426,"created_at":436},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":19,"substance":16,"depth":161,"authority":264,"freshness":20,"relevant":21,"comment":377},"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。",[379,380],{"name":371,"url":372},{"name":371,"url":381},"https:\u002F\u002Fajeee.co.in\u002Findex.php\u002Fajeee\u002Farticle\u002Fdownload\u002F6754\u002F5489",[26,27,383,384],"水资源管理","地下水监测",{"code":39,"slug":386,"name":387,"path":388,"parent_code":173},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":176,"locked":13,"version":34,"method":35,"evidence":391},{"candidates":392,"agricultural_evidence":40},[393,397,401,405,410,415],{"code":39,"score":105,"supported":40,"title":394,"abstract":395,"tags":396},[42],[26,42],[26],{"code":46,"score":110,"supported":40,"title":398,"abstract":399,"tags":400},[],[27,48],[27],{"code":190,"score":81,"supported":40,"title":402,"abstract":403,"tags":404},[],[193,194],[],{"code":197,"score":264,"supported":40,"title":406,"abstract":407,"tags":409},[],[408],"水资源",[383],{"code":52,"score":198,"supported":40,"title":411,"abstract":412,"tags":414},[],[413],"空间分析",[],{"code":416,"score":198,"supported":40,"title":417,"abstract":418,"tags":420},"I7",[],[419],"methodological framework",[],[422,423],"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":425,"openalex_id":427,"authors":428,"venue":371,"cited_by_count":75,"oa_url":372,"card":431,"direction":77,"ingested_from":78},"W7222805722",[429],{"name":430,"orcid":8},"Prof. I Pandu Ranga Reddy Dr. Hari Ramavath",{"tldr":432,"method":433,"finding":434,"direction":77,"opportunity":435},"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":438,"title":439,"url":440,"summary":441,"summary_zh":442,"content":8,"source_name":443,"source_url":440,"published_at":444,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":445,"score_detail":446,"sources":448,"tags":450,"primary_topic":454,"secondary_topics":455,"classification":460,"search_phrases":487,"slug":490,"view_count":75,"doi":491,"paper":492,"created_at":510},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",60,{"impact":19,"substance":17,"depth":105,"authority":12,"freshness":20,"relevant":21,"comment":447},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[449],{"name":443,"url":440},[27,28,451,452,453],"农用地适宜性","土地覆被","孟加拉农业",{"code":52,"slug":170,"name":171,"path":172,"parent_code":173},[456],{"code":46,"slug":457,"name":458,"path":459,"parent_code":173},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":176,"locked":13,"version":34,"method":35,"evidence":461},{"candidates":462,"agricultural_evidence":40},[463,467,471,476,483],{"code":52,"score":17,"supported":40,"title":464,"abstract":465,"tags":466},[28],[182,28,183],[28],{"code":46,"score":105,"supported":40,"title":468,"abstract":469,"tags":470},[48],[27,48],[27],{"code":325,"score":81,"supported":40,"title":472,"abstract":473,"tags":475},[],[474,330],"稻米",[],{"code":477,"score":81,"supported":40,"title":478,"abstract":479,"tags":482},"G",[],[480,481],"农村发展","rural development",[],{"code":190,"score":81,"supported":40,"title":484,"abstract":485,"tags":486},[],[193,194],[],[488,489],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":491,"openalex_id":493,"authors":494,"venue":504,"cited_by_count":75,"oa_url":440,"card":505,"direction":77,"ingested_from":78},"W7222895468",[495,497,499,501],{"name":496,"orcid":8},"Raju Ahmed",{"name":498,"orcid":8},"Shaikh Mehdee Mohammad",{"name":500,"orcid":8},"Ummul Momanin Coalee",{"name":502,"orcid":503},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":506,"method":507,"finding":508,"direction":77,"opportunity":509},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z"]