[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4361":3,"related-4361":86},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":55,"slug":58,"view_count":50,"doi":59,"paper":60,"created_at":85},4361,"Flood susceptibility assessment using GIS and an analytical hierarchy process in Ilorin Metropolis, Kwara, Nigeria","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44327-026-00380-3","Flooding remains a major environmental challenge in Ilorin Metropolis, Kwara State, Nigeria. In this study, flood susceptibility across the metropolis was assessed using geographic information systems (GIS), remote sensing, and the analytical hierarchy process (AHP). ASTER Global Digital Elevation Model and Landsat 8 OLI\u002FTIRS imagery acquired in January 2024 were used to derive nine flood-conditioning factors: elevation, slope, drainage density, topographic wetness index, stream power index, distance from streams, distance from roads, land use\u002Fland cover (LULC), and normalized difference vegetation index. The factors were weighted using the AHP and integrated through weighted overlay in ArcGIS. The resulting flood susceptibility map classified 0.8% of the study area as very low susceptibility, 9.3% as low, 47.1% as moderate, 30.8% as high, and 12.0% as very high. Thus, 42.8% of Ilorin Metropolis was classified as having high or very high flood susceptibility, particularly along the Asa River corridor and low-lying urban areas. Independent validation using 200 flood and non-flood reference points produced an overall accuracy of 96.5%, a sensitivity (recall) of 1.00, a specificity of 0.95, a precision of 0.895, and a Cohen’s kappa of 0.919. The results demonstrate the utility of the GIS-AHP framework for identifying flood-susceptible areas and supporting urban flood mitigation and land use planning in the Ilorin Metropolis.","洪水仍然是尼日利亚夸拉州伊洛林大都市面临的主要环境挑战。本研究利用地理信息系统（GIS）、遥感和层次分析法（AHP）评估了该大都市的洪水敏感性。研究使用ASTER全球数字高程模型和2024年1月获取的Landsat 8 OLI\u002FTIRS影像，提取了九个洪水致灾因子：高程、坡度、河网密度、地形湿度指数、河流功率指数、距河流距离、距道路距离、土地利用\u002F土地覆盖（LULC）以及归一化植被指数。采用层次分析法对这些因子进行赋权，并在ArcGIS中通过加权叠加进行集成。生成的洪水敏感性图将研究区0.8%的区域划分为极低敏感性，9.3%为低敏感性，47.1%为中等敏感性，30.8%为高敏感性，12.0%为极高敏感性。因此，伊洛林大都市42.8%的区域被归类为高或极高洪水敏感性，尤其是沿阿萨河走廊和低洼城市地区。利用200个洪水和非洪水参考点进行的独立验证得出总体精度为96.5%，灵敏度（召回率）为1.00，特异度为0.95，精确率为0.895，Cohen's kappa系数为0.919。结果表明，GIS-AHP框架可用于识别洪水易发区域，并为伊洛林大都市的城市洪水缓解和土地利用规划提供支持。",null,"Discover Cities","2026-10-05T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦尼日利亚伊洛林城市洪水易发性评估，属城市防灾与GIS遥感应用，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24],"遥感","GIS","洪水风险","城市防洪",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":54},[32,41,48],{"code":33,"score":34,"supported":35,"title":36,"abstract":37,"tags":40},"I4",18,true,[22],[38,22,39],"地理信息","geographic information",[22],{"code":42,"score":43,"supported":35,"title":44,"abstract":45,"tags":47},"I3",7,[],[21,46],"remote sensing",[21],{"code":49,"score":50,"supported":14,"title":51,"abstract":52,"tags":53},"H7",1,[],[],[23],"农业相关性需要复核",[56,57],"城市防洪 洪水风险 遥感 GIS","城市防洪 洪水风险","城市防洪洪水风险遥感GIS-4361","10.1007\u002Fs44327-026-00380-3",{"doi":59,"openalex_id":61,"authors":62,"venue":10,"cited_by_count":15,"oa_url":77,"card":78,"direction":82,"ingested_from":84},"W7220433564",[63,65,68,71,74],{"name":64,"orcid":9},"Olaitan Isioye",{"name":66,"orcid":67},"Maimuna Mahmud Zailani","https:\u002F\u002Forcid.org\u002F0009-0003-0566-4842",{"name":69,"orcid":70},"Mudassir Hassan","https:\u002F\u002Forcid.org\u002F0009-0005-0257-298X",{"name":72,"orcid":73},"Ibrahim Faruk Gaya","https:\u002F\u002Forcid.org\u002F0000-0003-2644-1982",{"name":75,"orcid":76},"Muhammad Lawal Abubakar","https:\u002F\u002Forcid.org\u002F0000-0002-2797-3166","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44327-026-00380-3.pdf",{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"用GIS、遥感和AHP评估尼日利亚伊洛林市洪水易发性并制图。","ASTER DEM与Landsat 8影像提取9个因子，AHP加权叠加，200点","42.8%区域高或极高易发，模型总体精度96.5%，Kappa为0.919。","农业遥感与作物表型","可引入机器学习与实时降雨数据，提升洪水易发性动态预测精度。","openalex","2026-10-07T23:30:28.711328Z",{"total":87,"page":50,"page_size":87,"items":88},6,[89,168,216,282,353,425],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":98,"sources":104,"tags":106,"primary_topic":109,"secondary_topics":114,"classification":119,"search_phrases":144,"slug":147,"view_count":140,"doi":148,"paper":149,"created_at":167},2673,"Flood Hazard Zone Mapping of Vashishti River Basin by Using Remote Sensing and GIS Techniques","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95677","Floods are among the most destructive natural hazards in regions characterised by high and intense rainfall, frequently causing substantial loss of human life and considerable damage to agriculture, infrastructure, property, and the natural environment. The Vashishti River basin, situated within the ecologically sensitive Western Ghats, is particularly susceptible to recurrent and severe flooding owing to its distinctive hydro-meteorological and physiographic characteristics. The interaction of intense monsoonal rainfall, basin topography, drainage conditions, and increasing human intervention further contributes to the basin's flood susceptibility. In this context, flood hazard mapping provides an effective means of identifying areas with varying levels of exposure to flooding and can support the reduction of disaster risks associated with human settlements, agricultural activities, infrastructure development, and, in particular, expanding urban areas. Therefore, the present study aims to identify, delineate, and characterise flood hazard zones within the Vashishti River basin. The resulting spatial information is expected to provide a scientific basis for improved flood risk assessment, land-use planning, disaster preparedness, and the formulation of effective flood mitigation and management strategies. A total of ten flood-influencing factors, namely elevation, slope, drainage density, distance from the river, land use\u002Fland cover, rainfall, runoff derived using the SCS-CN method, geomorphology, lithology, and soil texture, were analysed in a Geographic Information System (GIS). Weights were assigned using the Analytic Hierarchy Process (AHP). With a consistency ratio (CR) of 0.06 for the correlation matrix, the flood hazard zone map indicates that 0.81% of the study area falls within a very low hazard zone, 13.49% within a low hazard zone, 47.90% within a moderate zone, 20.59% within a high flood hazard zone, and 17.20% within a very high flood hazard zone. Rainfall, elevation, and slope are the primary factors influencing flood risk. The AHP-based approach used in this study provides a practical framework for flood hazard mapping and may be applied to other regions for flood hazard management and mitigation.","洪水是降雨量大且强度高的地区最具破坏性的自然灾害之一，常造成重大人员伤亡以及对农业、基础设施、财产和自然环境的严重破坏。瓦希什蒂河流域位于生态敏感的西高止山脉地区，由于其独特的水文气象和地形特征，特别容易遭受反复且严重的洪水。强烈的季风降雨、流域地形、排水条件以及日益增加的人类干预相互作用，进一步加剧了该流域的洪水易发性。在此背景下，洪水灾害制图为识别不同洪水暴露程度区域提供了有效手段，并可支持降低与人类住区、农业活动、基础设施建设以及特别是不断扩张的城市区域相关的灾害风险。因此，本研究旨在识别、划定并表征瓦希什蒂河流域内的洪水灾害区。所生成的空间信息预期可为改进洪水风险评估、土地利用规划、灾害防备以及制定有效的洪水减缓与管理策略提供科学依据。研究在地理信息系统（GIS）中分析了十个洪水影响因子，即高程、坡度、河网密度、距河流距离、土地利用\u002F土地覆被、降雨、采用SCS-CN方法得出的径流、地貌、岩性和土壤质地。采用层次分析法（AHP）确定权重。相关矩阵的一致性比率（CR）为0.06，洪水灾害区图表明，研究区0.81%属于极低灾害区，13.49%属于低灾害区，47.90%属于中等灾害区，20.59%属于高洪水灾害区，17.20%属于极高洪水灾害区。降雨、高程和坡度是影响洪水风险的主要因素。本研究采用的基于AHP的方法为洪水灾害制图提供了实用框架，并可应用于其他地区的洪水灾害管理与减缓。","International Journal of Environment and Climate Change","2026-09-15T00:00:00Z",67,{"impact":99,"substance":34,"depth":100,"authority":101,"freshness":102,"relevant":50,"comment":103},12,16,13,8,"基于遥感与GIS的流域洪水危险性制图研究，方法规范、数据翔实，对农业防灾减灾与土地利用规划有参考价值，但属区域性案例研究，公共影响有限。",[105],{"name":95,"url":92},[21,107,22,108,23],"农业防灾","流域治理",{"code":33,"slug":110,"name":111,"path":112,"parent_code":113},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[115],{"code":42,"slug":116,"name":117,"path":118,"parent_code":113},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":120,"locked":14,"version":28,"method":29,"evidence":121},"classified",{"candidates":122,"agricultural_evidence":35},[123,127,132,139],{"code":33,"score":34,"supported":35,"title":124,"abstract":125,"tags":126},[22],[38,22,39],[22],{"code":42,"score":128,"supported":35,"title":129,"abstract":130,"tags":131},9,[46],[],[21],{"code":133,"score":87,"supported":35,"title":134,"abstract":135,"tags":138},"H2",[],[136,137],"土壤","soil",[],{"code":49,"score":140,"supported":14,"title":141,"abstract":142,"tags":143},2,[],[],[107,23],[145,146],"农业防灾 洪水风险 流域治理 遥感","农业防灾 洪水风险","农业防灾洪水风险流域治理遥感-2673","10.9734\u002Fijecc\u002F2026\u002Fv16i95677",{"doi":148,"openalex_id":150,"authors":151,"venue":95,"cited_by_count":15,"oa_url":92,"card":162,"direction":82,"ingested_from":84},"W7213314416",[152,154,156,158,160],{"name":153,"orcid":9},"Bhagyalaxmi Kallihal",{"name":155,"orcid":9},"S. S. Nagarkar",{"name":157,"orcid":9},"B. L. Ayare",{"name":159,"orcid":9},"S.T. Patil",{"name":161,"orcid":9},"S. V. Deshmukh",{"tldr":163,"method":164,"finding":165,"direction":82,"opportunity":166},"用遥感与GIS结合AHP方法，对Vashishti河流域进行洪水灾害分区制图。","遥感与GIS空间分析，选取10个洪水影响因子，AHP赋权（CR=0.06）。","47.90%区域属中等危险区，17.20%为极高危险区；降雨、高程和坡度是主控因子。","可结合作物分布与农情数据，将洪水危险分区延伸至农田尺度风险评估与减灾决策。","2026-09-16T23:30:30.712391Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":176,"sources":178,"tags":180,"primary_topic":9,"secondary_topics":181,"classification":182,"search_phrases":197,"slug":200,"view_count":201,"doi":202,"paper":203,"created_at":215},2310,"Geospatial Analysis of Topographic and Hydrological Controls on Flood Susceptibility","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.12.no3.2026.pg279.299","This study evaluates the influence of topographic and hydrological factors on flood susceptibility in the South–South region of Nigeria using Geographic Information Systems (GIS), Remote Sensing, and the Analytical Hierarchy Process (AHP). Digital Elevation Model (DEM), slope, aspect, drainage density, stream density, flow direction, and distance from river variables were analysed to determine their contribution to flooding. The results show that elevation ranges from approximately 13 m in low-lying coastal areas (Delta, Rivers, Bayelsa, and Akwa Ibom States) to about 256 m in elevated inland areas (Cross River and parts of Edo State). Slope analysis indicates that coastal areas are dominated by very gentle slopes (0°–3.37°), which promote water accumulation and increase flood susceptibility. Aspect results further reveal that much of the coastal region is predominantly flat (−1), limiting directional flow and enhancing water stagnation. Hydrological analysis shows that drainage density is mainly very low to moderate (0 89.89 km\u002Fkm²), indicating high infiltration but increased water retention, while localized high values (89.9–134.8 km\u002Fkm²) enhance runoff. Stream density is highest (228.1–811 km\u002Fkm²) in Delta, Bayelsa, and Rivers States, whereas lower values occur in Edo and Cross River. Flow direction exhibits a dominant east–west pattern, with accumulation values ranging from 1–16, indicating runoff convergence. Distance-to-river analysis shows that large areas lie within 0 1,000 m of river channels. Flood susceptibility is highest in coastal zones and lowest in inland elevated areas. The study demonstrates that terrain morphology and hydrological network characteristics are key determinants of flooding in the region and highlights the effectiveness of geospatial techniques in flood susceptibility assessment and management in the South–South region of Nigeria","本研究利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），评估了地形与水文因素对尼日利亚南南地区洪水易发性的影响。通过分析数字高程模型（DEM）、坡度、坡向、河网密度、河流密度、流向及距河流距离等变量，确定其对洪水的贡献。结果表明，高程从沿海低洼地区（三角洲州、河流州、巴耶尔萨州和阿夸伊博姆州）约13 m，到内陆高地区域（克罗斯河州及埃多州部分地区）约256 m不等。坡度分析表明，沿海地区以极缓坡（0°–3.37°）为主，有利于水分积聚并增加洪水易发性。坡向结果进一步显示，沿海大部分区域以平坦（−1）为主，限制了定向径流并加剧了水分滞留。水文分析表明，河网密度主要为极低至中等（0–89.89 km\u002Fkm²），表明入渗率高但持水量增加，而局部高值（89.9–134.8 km\u002Fkm²）则增强径流。河流密度在三角洲州、巴耶尔萨州和河流州最高（228.1–811 km\u002Fkm²），而在埃多州和克罗斯河州较低。流向呈现主导性的东西向格局，汇流累积值在1–16之间，表明径流汇聚。距河流距离分析显示，大片区域位于河道0–1,000 m范围内。洪水易发性在沿海地带最高，在内陆高地区域最低。研究表明，地形形态和水文网络特征是该地区洪水的关键决定因素，并凸显了地理空间技术在尼日利亚南南地区洪水易发性评估与管理中的有效性。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-10T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":177},"该研究聚焦尼日利亚洪水易发性评估，属地理环境领域，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[179],{"name":174,"url":171},[21,22,23],[],{"status":27,"locked":14,"version":28,"method":29,"evidence":183},{"candidates":184,"agricultural_evidence":14,"reason":54},[185,189,193],{"code":33,"score":13,"supported":35,"title":186,"abstract":187,"tags":188},[],[38,22,39],[22],{"code":42,"score":43,"supported":35,"title":190,"abstract":191,"tags":192},[],[21,46],[21],{"code":49,"score":50,"supported":14,"title":194,"abstract":195,"tags":196},[],[],[23],[198,199],"洪水风险 遥感 GIS","洪水风险 遥感","洪水风险遥感GIS-2310",3,"10.56201\u002Fijgem.vol.12.no3.2026.pg279.299",{"doi":202,"openalex_id":204,"authors":205,"venue":174,"cited_by_count":15,"oa_url":208,"card":209,"direction":214,"ingested_from":84},"W7212169235",[206],{"name":207,"orcid":9},"Anthony Chikaike Nwinye-Harrison","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 12 NO. 3 2026\u002FGEOSPATIAL ANALYSIS OF TOPOGRAPHIC 279-299.pdf",{"tldr":210,"method":211,"finding":212,"direction":82,"opportunity":213},"用GIS、遥感和AHP评估尼日利亚南部地形与水文因子对洪水易发性的影响。","GIS、遥感、AHP，基于DEM、坡度、坡向、河网密度等因子分析。","沿海低平区洪水易发性最高，内陆高地区最低，地形与水文网络是关键决定因素。","可结合实时降雨与土地利用变化，构建动态洪水易发性模型并服务农业风险管理。","智慧农业 \u002F 农业物联网","2026-09-13T23:30:12.636598Z",{"id":217,"title":218,"url":219,"summary":220,"summary_zh":221,"content":9,"source_name":222,"source_url":219,"published_at":223,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":224,"score_detail":225,"sources":228,"tags":230,"primary_topic":234,"secondary_topics":235,"classification":236,"search_phrases":262,"slug":265,"view_count":15,"doi":266,"paper":267,"created_at":281},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":102,"substance":34,"depth":226,"authority":99,"freshness":128,"relevant":50,"comment":227},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。",[229],{"name":222,"url":219},[21,231,232,22,233],"土地利用","土壤侵蚀","水土保持",{"code":33,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":120,"locked":14,"version":28,"method":29,"evidence":237},{"candidates":238,"agricultural_evidence":35},[239,243,247,251,257],{"code":33,"score":13,"supported":35,"title":240,"abstract":241,"tags":242},[],[38,22,39],[22],{"code":42,"score":43,"supported":35,"title":244,"abstract":245,"tags":246},[],[21,46],[21],{"code":133,"score":87,"supported":35,"title":248,"abstract":249,"tags":250},[],[136,137],[],{"code":252,"score":201,"supported":35,"title":253,"abstract":254,"tags":256},"H3",[],[255],"灌溉",[],{"code":258,"score":140,"supported":14,"title":259,"abstract":260,"tags":261},"H6",[],[],[231,233],[263,264],"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":266,"openalex_id":268,"authors":269,"venue":222,"cited_by_count":15,"oa_url":9,"card":275,"direction":82,"ingested_from":84},"W7223117733",[270,272],{"name":271,"orcid":9},"MERİÇ KURT",{"name":273,"orcid":274},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":276,"method":277,"finding":278,"direction":279,"opportunity":280},"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":283,"title":284,"url":285,"summary":286,"summary_zh":287,"content":9,"source_name":288,"source_url":285,"published_at":289,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":290,"score_detail":291,"sources":295,"tags":297,"primary_topic":301,"secondary_topics":305,"classification":306,"search_phrases":332,"slug":335,"view_count":15,"doi":336,"paper":337,"created_at":352},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":99,"substance":292,"depth":293,"authority":99,"freshness":128,"relevant":50,"comment":294},20,17,"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。",[296],{"name":288,"url":285},[298,21,22,299,300],"农业人工智能","野生动物保护","人兽冲突",{"code":113,"slug":302,"name":303,"path":304,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":307,"locked":14,"version":28,"method":29,"evidence":308},"partial",{"candidates":309,"agricultural_evidence":35,"reason":331},[310,318,322,327],{"code":311,"score":13,"supported":35,"title":312,"abstract":313,"tags":317},"I2",[],[314,315,316],"机器学习","machine learning","decision support",[298],{"code":33,"score":13,"supported":35,"title":319,"abstract":320,"tags":321},[],[38,22,39],[22],{"code":42,"score":323,"supported":35,"title":324,"abstract":325,"tags":326},4,[],[21],[21],{"code":258,"score":50,"supported":14,"title":328,"abstract":329,"tags":330},[],[],[299],"一级领域明确，二级方向需要细分",[333,334],"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":336,"openalex_id":338,"authors":339,"venue":288,"cited_by_count":15,"oa_url":285,"card":346,"direction":351,"ingested_from":84},"W7222999966",[340,342,344],{"name":341,"orcid":9},"Bhutia Dechen Doma",{"name":343,"orcid":9},"Swarup Das",{"name":345,"orcid":9},"Mandal Rakesh Kumar",{"tldr":347,"method":348,"finding":349,"direction":82,"opportunity":350},"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":354,"title":355,"url":356,"summary":357,"summary_zh":358,"content":9,"source_name":359,"source_url":356,"published_at":360,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":361,"score_detail":362,"sources":365,"tags":367,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":408,"slug":411,"view_count":15,"doi":412,"paper":413,"created_at":424},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":102,"substance":100,"depth":363,"authority":201,"freshness":102,"relevant":50,"comment":364},15,"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。",[366],{"name":359,"url":356},[368,21,22,369,370],"决策支持系统","水稻种植","土地评价",{"code":113,"slug":302,"name":303,"path":304,"parent_code":9},[],{"status":307,"locked":14,"version":28,"method":29,"evidence":374},{"candidates":375,"agricultural_evidence":35,"reason":331},[376,380,384,393,400,404],{"code":311,"score":99,"supported":35,"title":377,"abstract":378,"tags":379},[316],[316],[368],{"code":33,"score":99,"supported":35,"title":381,"abstract":382,"tags":383},[22],[22],[22],{"code":385,"score":13,"supported":35,"title":386,"abstract":387,"tags":392},"A1",[],[388,389,390,391],"水稻","稻田","rice","paddy",[369],{"code":394,"score":87,"supported":35,"title":395,"abstract":396,"tags":399},"A8",[],[397,398],"粮食安全","food security",[],{"code":133,"score":87,"supported":35,"title":401,"abstract":402,"tags":403},[],[136,137],[],{"code":42,"score":50,"supported":14,"title":405,"abstract":406,"tags":407},[],[],[21],[409,410],"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":412,"openalex_id":414,"authors":415,"venue":359,"cited_by_count":15,"oa_url":356,"card":419,"direction":351,"ingested_from":84},"W7222963628",[416],{"name":417,"orcid":418},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":420,"method":421,"finding":422,"direction":82,"opportunity":423},"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":426,"title":427,"url":428,"summary":429,"summary_zh":430,"content":9,"source_name":431,"source_url":428,"published_at":432,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":433,"score_detail":434,"sources":436,"tags":438,"primary_topic":442,"secondary_topics":443,"classification":445,"search_phrases":472,"slug":475,"view_count":15,"doi":476,"paper":477,"created_at":495},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":102,"substance":34,"depth":363,"authority":13,"freshness":128,"relevant":50,"comment":435},"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。",[437],{"name":431,"url":428},[21,22,439,440,441],"农用地适宜性","土地覆被","孟加拉农业",{"code":33,"slug":110,"name":111,"path":112,"parent_code":113},[444],{"code":42,"slug":116,"name":117,"path":118,"parent_code":113},{"status":120,"locked":14,"version":28,"method":29,"evidence":446},{"candidates":447,"agricultural_evidence":35},[448,452,456,461,468],{"code":33,"score":34,"supported":35,"title":449,"abstract":450,"tags":451},[22],[38,22,39],[22],{"code":42,"score":363,"supported":35,"title":453,"abstract":454,"tags":455},[46],[21,46],[21],{"code":385,"score":87,"supported":35,"title":457,"abstract":458,"tags":460},[],[459,390],"稻米",[],{"code":462,"score":87,"supported":35,"title":463,"abstract":464,"tags":467},"G",[],[465,466],"农村发展","rural development",[],{"code":133,"score":87,"supported":35,"title":469,"abstract":470,"tags":471},[],[136,137],[],[473,474],"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":476,"openalex_id":478,"authors":479,"venue":489,"cited_by_count":15,"oa_url":428,"card":490,"direction":82,"ingested_from":84},"W7222895468",[480,482,484,486],{"name":481,"orcid":9},"Raju Ahmed",{"name":483,"orcid":9},"Shaikh Mehdee Mohammad",{"name":485,"orcid":9},"Ummul Momanin Coalee",{"name":487,"orcid":488},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":491,"method":492,"finding":493,"direction":82,"opportunity":494},"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"]