[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4527":3,"related-4527":107},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":45,"search_phrases":80,"slug":83,"view_count":22,"doi":84,"paper":85,"created_at":106},4527,"Socio‐Economic Impact of Sustainable Development of Water Resources in the Semi‐Arid Regions of Village Raleras, Maharashtra: A Case Study Using Machine Learning Techniques","https:\u002F\u002Fdoi.org\u002F10.1111\u002F1477-8947.70100","ABSTRACT From a socio‐economic perspective, it is of interest to find out whether sustainable water resources can truly sustain rural livelihoods. Based on historical research, it is known that an improvement on resilient economic indicators such as ground water is often correlated with socio‐economic improvements and other aspects of rural livelihoods. The extent of factors affecting livelihood sustenance and improvement in the village of Raleras, Maharashtra, aids in creating a definite understanding of the facets correlating with groundwater restoration. Subsistence channels, arising as an outcome of the created impoundment structures, are tested for their impact on income gains and livelihood improvement. Examination on collected opinion was carried out to provide insights into the ‘before’ and ‘after’ (2015–2023) conditions. A five‐capitals framework has been employed for the interpretation of these results, along with a multivariate analysis to predict a causal framework for the improvement of livelihoods in agrarian economies through creation of check dams and watershed management. Time series study shows that there is greater variance in the NDVI progressively from 2021 to 2023 with values ranging from 0.25 to 0.50 in 2015, to 0.50–0.75 by 2021. Further increase in green patches in 2023 indicates enhanced vegetation health especially in the northern regions. Alternately, NDWI values elaborate initial severe water stress (−0.25 to 0.00) with progressive improvements in localized areas. The spatial variation map shows that eventually there is a notable improvement with more land supporting shrubbery along with more water bodies. The prominent increase in vegetation particularly in the later years can be attributed to water management initiatives undertaken in Raleras. The distributions of the annual average revenue from agricultural operations in 2015 (M = 146,907, SD = 95445.8) and 2023 (M = 706860.5, SD = 655748.3) showed a statistically significant improvement [ t (214) = 13.06, p \u003C 0.001]. The low p ‐value of 0.001205 ( p \u003C 0.05) of the educational facilities utilised indicates a statistically significant result, proving the association between educational quality and improved water facilities. All of the variables under evaluation, including the respondents' vehicle type, landholding type, and house type, have large and positive chi‐squared values, suggesting that the ‘before’ and ‘after’ distributions for each of these variables differ. Principal Component Analysis is used to summarise correlated livelihood indicators and reveals that income growth and livelihood improvement is multi‐dimensional. Multiple pathways to income change are identified through Partial Least Squares Regression, including agriculture and market‐linked channels. A Random Forest Regression Model is utilised to assess the relative importance of livelihood channels and baseline characteristics. Results indicate that improvement in irrigation access, crop diversification, housing quality, livestock ownership and female literacy are strongly associated with higher income gains. Hence, it may be said that the groundwater recharge as a result of construction of check dams must have improved agricultural activity or agricultural output in a material manner, and accordingly all capitals for Raleras have improved.","从社会经济视角来看，探究可持续水资源能否真正维持农村生计具有重要意义。基于历史研究可知，地下水等韧性经济指标的改善往往与社会经济进步及农村生计的其他方面相关联。马哈拉施特拉邦拉莱拉斯村影响生计维持与改善的因素程度，有助于明确理解与地下水恢复相关的各个维度。作为所建蓄水设施产物的生计渠道，其对面收入增长和生计改善的影响得到了检验。对收集到的意见进行了考察，以深入了解“之前”和“之后”（2015—2023年）的状况。研究采用了五资本框架来解读这些结果，并结合多变量分析，预测通过修建拦水坝和流域管理来改善农业经济生计的因果框架。时间序列研究表明，从2021年到2023年，NDVI的变异性逐渐增大，2015年数值范围为0.25至0.50，到2021年升至0.50至0.75。2023年绿色斑块进一步增加，表明植被健康状况改善，尤其是在北部地区。另一方面，NDWI值显示初期存在严重的水分胁迫（−0.25至0.00），后在局部地区逐步改善。空间变异图显示，最终出现了显著改善，更多土地能够支持灌木生长，同时水体也有所增加。植被的显著增加，尤其是在后期，可归因于拉莱拉斯村所实施的水资源管理举措。2015年（M = 146,907，SD = 95445.8）和2023年（M = 706860.5，SD = 655748.3）农业经营年均收入的分布显示出统计学上显著的改善[t(214) = 13.06，p \u003C 0.001]。所利用教育设施的p值为0.001205（p \u003C 0.05），表明结果具有统计学显著性，证明了教育质量与改善的供水设施之间的关联。所有受评估变量，包括受访者的车辆类型、土地持有类型和房屋类型，均具有较大且正向的卡方值，表明这些变量各自的“之前”和“之后”分布存在差异。",null,"Natural Resources Forum","2026-10-07T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"以机器学习与遥感数据量化印度半干旱村庄水资源可持续性对生计的影响，方法扎实、结论具体，但属境外案例研究，对国内三农信息化仅有方法借鉴价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"乡村振兴","机器学习","水资源管理","遥感监测","灌溉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":53},[51,58,63,70,75],{"code":33,"score":52,"supported":53,"title":54,"abstract":56,"tags":57},9,true,[55],"machine learning",[],[28],{"code":40,"score":21,"supported":53,"title":59,"abstract":60,"tags":62},[],[61,41],"水资源",[29,31],{"code":64,"score":65,"supported":53,"title":66,"abstract":67,"tags":69},"C",3,[],[68],"livestock",[],{"code":71,"score":22,"supported":14,"title":72,"abstract":73,"tags":74},"G",[],[],[27],{"code":76,"score":22,"supported":14,"title":77,"abstract":78,"tags":79},"I3",[],[],[30],[81,82],"Raleras 马哈拉施特拉 水资源","check dam 地下水 生计","Raleras马哈拉施特拉水资源-4527","10.1111\u002F1477-8947.70100",{"doi":84,"openalex_id":86,"authors":87,"venue":10,"cited_by_count":97,"oa_url":9,"card":98,"direction":104,"ingested_from":105},"W7220922038",[88,91,93,95],{"name":89,"orcid":90},"Papiya Deb","https:\u002F\u002Forcid.org\u002F0000-0002-6554-5111",{"name":92,"orcid":9},"Roopsha Deb",{"name":94,"orcid":9},"Udaysinh Bhosale",{"name":96,"orcid":9},"Vemula Sai Vikas",0,{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"用机器学习评估印度半干旱村庄水资源可持续开发对农村生计的社会经济影响。","NDVI\u002FNDWI遥感、五资本框架、多元分析与卡方检验，2015–2023数据。","水管理举措显著提升植被覆盖与农业收入，教育设施改善与水资源改善显著相关。","数字乡村与农业信息化","可结合遥感时序与农户调查，量化水利工程对生计的因果效应并推广至其他半干旱区。","农业人工智能与决策模型","openalex","2026-10-09T23:30:50.079298Z",{"total":108,"page":22,"page_size":108,"items":109},6,[110,165,249,321,394,479],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":117,"published_at":118,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":119,"score_detail":120,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":130,"classification":132,"search_phrases":146,"slug":149,"view_count":22,"doi":150,"paper":151,"created_at":164},4914,"Water Optimizer: A Tool for Decision Support and Policy Analysis","https:\u002F\u002Fdoi.org\u002F10.22004\u002Fag.econ.413426","Water Optimizer is a tool for analyzing alternative water management strategies when the available water supply is limited. It can be used by producers to determine the profit maximizing crops to produce and the optimum amount of water to apply to each crop, given a particular water supply. It can also be used to evaluate the economic consequences of public policies which expand or reduce the amount of irrigation.","水资源优化器是一种在可用供水有限时分析替代水资源管理策略的工具。生产者可用其确定在特定供水量下能实现利润最大化的作物种类及每种作物的最优灌溉水量，也可用于评估扩大或减少灌溉面积的公共政策的经济后果。","Lincoln (University of Nebraska)","https:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43","2026-10-13T00:00:00Z",67,{"impact":17,"substance":19,"depth":121,"authority":20,"freshness":21,"relevant":22,"comment":122},16,"内布拉斯加大学开发的灌溉决策支持工具，方法实用但属细分领域工具类论文，公共影响有限。",[124],{"name":116,"url":117},[126,29,127,128],"决策支持系统","灌溉决策","农业政策分析",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[131],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":133},{"candidates":134,"agricultural_evidence":53},[135,141],{"code":40,"score":136,"supported":53,"title":137,"abstract":138,"tags":140},11,[],[139,61,41],"灌溉",[29,127],{"code":33,"score":52,"supported":53,"title":142,"abstract":144,"tags":145},[143],"decision support",[],[126],[147,148],"Water Optimizer 灌溉决策","内布拉斯加大学 水资源管理","WaterOptimizer灌溉决策-4914","10.22004\u002Fag.econ.413426",{"doi":150,"openalex_id":152,"authors":153,"venue":116,"cited_by_count":97,"oa_url":158,"card":159,"direction":104,"ingested_from":105},"W38113320",[154,156],{"name":155,"orcid":9},"Derrel L. Martin",{"name":157,"orcid":9},"Ray Supalla","http:\u002F\u002Fdigitalcommons.unl.edu\u002Fageconworkpap\u002F43",{"tldr":160,"method":161,"finding":162,"direction":104,"opportunity":163},"开发了Water Optimizer工具，在有限供水下优化作物种植与灌溉量以最大化利润。","基于优化模型与决策支持工具，分析不同供水策略及政策的经济影响。","该工具能帮助生产者选择利润最大化作物和最优灌溉量，并评估灌溉政策的经济后果。","可结合实时气象与土壤数据，开发动态灌溉决策模型，并拓展至多目标（经济-环境）优化。","2026-10-14T23:30:55.832240Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":178,"tags":180,"primary_topic":185,"secondary_topics":191,"classification":194,"search_phrases":227,"slug":230,"view_count":231,"doi":232,"paper":233,"created_at":248},3825,"Hybrid Rule-Based and Machine Learning Decision-Support Framework for Smart Paddy Farming","https:\u002F\u002Fdoi.org\u002F10.4038\u002Fatrsj.v6i1.70","Paddy farming decisions are affected by changing weather conditions, water availability, soil conditions, and crop health, creating a need for decision-support approaches that combine agronomic knowledge with data-driven prediction. This research presents a hybrid rule-based and machine-learning decision-support solution for paddy management that combines interpretable agronomic rules and lightweight predictive models. The temporal alignment, interpolation, normalization, feature engineering, and crop-stage aggregation of weather and NDVI data were integrated and preprocessed. It is a rule-based engine with 63 expert rules that was fused with Decision Tree, Logistic Regression, and Random Forest models using a weighted decision-fusion mechanism. The hybrid model performed with 90% accuracy, 0.91 precision, 0.88 recall, and 0.89 F1-score, while the rule-based model had 72% accuracy, and the best standalone Random Forest model had 86% accuracy. The hybrid model also yielded the minimum RMSE of 7.6. The results of the simulation scenarios showed increases in yield of 3–17% and savings in irrigation-water use of 12.4%. There were 10 farmers and 2 agricultural officers who participated in the preliminary usability test and were scored from 4.0 to 4.8 out of 5. The key is an adaptive machine-learning prediction integrated with clear agronomic logic, in a lightweight advisory system. The yields and water-saving benefits, however, are simulated, and experimental testing on a wider range of locations and seasons will be needed to determine the full agronomic benefits.","水稻种植决策受到天气条件变化、水资源可用性、土壤条件和作物健康状况的影响，因此需要将农艺知识与数据驱动预测相结合的决策支持方法。本研究提出了一种基于规则与机器学习混合的水稻管理决策支持方案，将可解释的农艺规则与轻量级预测模型相结合。研究对天气和NDVI数据进行了时间对齐、插值、归一化、特征工程和作物阶段聚合的集成与预处理。构建了一个包含63条专家规则的基于规则的引擎，并通过加权决策融合机制将其与决策树、逻辑回归和随机森林模型相融合。混合模型的准确率为90%，精确率为0.91，召回率为0.88，F1分数为0.89，而基于规则的模型准确率为72%，最佳独立随机森林模型准确率为86%。混合模型还取得了最低的RMSE值7.6。模拟情景结果显示，产量提高了3%–17%，灌溉用水节省了12.4%。共有10名农民和2名农业官员参与了初步可用性测试，评分为4.0至4.8分（满分5分）。其核心在于将自适应机器学习预测与清晰的农艺逻辑相结合，构建一个轻量级咨询系统。然而，产量提升和节水效益均为模拟结果，需要在更广泛的地点和季节进行实验测试，以确定其完整的农艺效益。","Journal of Agro-Technology and Rural Sciences","2026-09-29T00:00:00Z",68,{"impact":17,"substance":175,"depth":176,"authority":13,"freshness":52,"relevant":22,"comment":177},20,17,"提出规则与机器学习融合的水稻决策支持框架，指标与仿真结果具体，但验证样本小、增产节水为模拟值，属细分领域方法学进展。",[179],{"name":171,"url":168},[181,182,30,183,184],"智慧农业","农业人工智能","水稻种植","灌溉节水",{"code":186,"slug":187,"name":188,"path":189,"parent_code":190},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[192,193],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":195},{"candidates":196,"agricultural_evidence":53},[197,203,207,211,218,223],{"code":186,"score":198,"supported":53,"title":199,"abstract":201,"tags":202},15,[200],"paddy",[188,200],[183],{"code":33,"score":17,"supported":53,"title":204,"abstract":205,"tags":206},[55],[28],[182],{"code":40,"score":52,"supported":53,"title":208,"abstract":209,"tags":210},[],[139,61,41],[],{"code":212,"score":108,"supported":53,"title":213,"abstract":214,"tags":217},"H2",[],[215,216],"土壤","soil",[],{"code":219,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},"I1",[],[],[181],{"code":76,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},[],[],[30],[228,229],"水稻 决策支持 机器学习","NDVI 水稻 灌溉","水稻决策支持机器学习-3825",2,"10.4038\u002Fatrsj.v6i1.70",{"doi":232,"openalex_id":234,"authors":235,"venue":171,"cited_by_count":97,"oa_url":241,"card":242,"direction":247,"ingested_from":105},"W7214735276",[236,239],{"name":237,"orcid":238},"Sawjanya Sathyaseelan","https:\u002F\u002Forcid.org\u002F0009-0003-1194-527X",{"name":240,"orcid":9},"K. Sriram","https:\u002F\u002Fatrsj.sljol.info\u002Farticles\u002F70\u002Ffiles\u002F6abb528a0792a.pdf",{"tldr":243,"method":244,"finding":245,"direction":104,"opportunity":246},"提出规则与机器学习混合的水稻决策支持框架，提升决策精度并节水增产。","融合63条专家规则与决策树、逻辑回归、随机森林，处理天气和NDVI数据。","混合模型准确率90%，模拟增产3-17%，节水12.4%，可用性评分4.0-4.8。","当前增产节水为模拟结果，未来需在多地点多季节开展田间试验验证农艺效益。","智慧农业 \u002F 农业物联网","2026-09-30T23:30:09.772909Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":256,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":257,"sources":259,"tags":261,"primary_topic":265,"secondary_topics":266,"classification":271,"search_phrases":302,"slug":305,"view_count":231,"doi":306,"paper":307,"created_at":320},3804,"Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos17100941","Accurate estimation of actual evapotranspiration (ETa) is essential for sustainable water resources management and agricultural planning. This study investigated how the availability of reference evapotranspiration (ET0) and soil moisture (SM) affects ETa prediction performance and predictor importance using Random Forest (RF), Bagging Trees (BT), Least Squares Boosting (LSBoost), Generalized Additive Models (GAM), and Multiple Linear Regression (MLR). Global solar radiation (Rs), land surface temperature (LST), normalized difference vegetation index (NDVI), and SM were evaluated under two primary scenarios, with and without ET0. Models were developed using 80% of the data, with five-fold cross-validation conducted within the training subset, and final performance was evaluated on an independent 20% test subset. Model interpretability was assessed using SHAP, Permutation Feature Importance (PFI), Partial Dependence Plot (PDP), and temporal signed-SHAP analyses, while an additional SM-ablation experiment quantified the incremental predictive contribution of soil moisture. In Scenario I, ET0 was the dominant predictor, and RF and BT achieved the highest independent-test performance (R2 = 0.875; RMSE = 0.398–0.399 mm day−1). Excluding ET0 reduced predictive performance, with RF, LSBoost, and BT achieving R2 values of approximately 0.80 in Scenario II, while the predictor-importance structure shifted primarily toward Rs, followed by SM, LST, and NDVI. Removal of SM further reduced performance in both scenarios, with substantially greater deterioration when ET0 was unavailable: R2 decreased by 0.025–0.044 in Scenario I and by 0.051–0.099 in Scenario II. Under the SM-excluded Scenario I configuration, which more closely aligned the predictor information with previous ANN- and SAFER-based studies, R2 values ranged from 0.820 to 0.832, indicating that differences in ETa prediction performance cannot be attributed solely to model structure but also depend strongly on predictor information content. Overall, ET0 provides substantial atmospheric-demand information for ETa prediction, whereas Rs assumes the dominant predictive role, and SM provides particularly important complementary information when ET0 is unavailable. The combined performance, ablation, and explainability analyses demonstrate the importance of considering predictor composition together with model structure when developing and comparing ETa estimation approaches.","准确估算实际蒸散发（ETa）对于可持续水资源管理和农业规划至关重要。本研究探讨了参考蒸散发（ET0）和土壤水分（SM）的可用性如何影响ETa预测性能及预测因子重要性，采用了随机森林（RF）、Bagging Trees（BT）、最小二乘提升（LSBoost）、广义加性模型（GAM）和多元线性回归（MLR）方法。在全球太阳辐射（Rs）、地表温度（LST）、归一化差异植被指数（NDVI）和SM的条件下，评估了包含和不包含ET0两种主要情景。模型使用80%的数据进行开发，在训练子集内进行五折交叉验证，最终性能在独立的20%测试子集上评估。模型可解释性通过SHAP、排列特征重要性（PFI）、偏依赖图（PDP）和时间符号SHAP分析进行评估，同时通过额外的SM消融实验量化了土壤水分的增量预测贡献。在情景I中，ET0是主导预测因子，RF和BT取得了最高的独立测试性能（R2 = 0.875；RMSE = 0.398–0.399 mm day−1）。排除ET0降低了预测性能，在情景II中RF、LSBoost和BT的R2值约为0.80，同时预测因子重要性结构主要转向Rs，其次是SM、LST和NDVI。去除SM进一步降低了两种情景下的性能，且在ET0不可用时性能下降幅度更大：情景I中R2下降了0.025–0.044，情景II中下降了0.051–0.099。在排除SM的情景I配置下（该配置使预测因子信息更接近此前的ANN和SAFER研究），R2值范围为0.820至0.832，表明ETa预测性能的差异不能仅归因于模型结构，还强烈依赖于预测因子信息含量。总体而言，ET0为ETa预测提供了大量大气需求信息，而Rs承担了主导预测作用，SM在ET0不可用时提供了特别重要的补充信息。综合性能、消融和可解释性分析表明，在开发和比较ETa估算方法时，需将预测因子组成与模型结构一并考虑。","Atmosphere","2026-09-27T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":258},"方法对比与可解释性分析扎实，对农业水资源管理和灌溉决策有参考价值，但属专业论文，公共影响有限。",[260],{"name":255,"url":252},[181,262,28,263,264],"农业遥感","土壤墒情","蒸散发估算",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[267,270],{"code":212,"slug":216,"name":268,"path":269,"parent_code":44},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":46,"locked":14,"version":47,"method":48,"evidence":272},{"candidates":273,"agricultural_evidence":53},[274,280,284,288,294,298],{"code":40,"score":176,"supported":53,"title":275,"abstract":277,"tags":279},[276],"evapotranspiration",[61,278,276],"蒸散发",[],{"code":212,"score":198,"supported":53,"title":281,"abstract":282,"tags":283},[216],[215,216],[263],{"code":33,"score":52,"supported":53,"title":285,"abstract":286,"tags":287},[55],[],[28],{"code":289,"score":65,"supported":53,"title":290,"abstract":291,"tags":293},"J1",[],[292],"农业规划",[],{"code":219,"score":22,"supported":14,"title":295,"abstract":296,"tags":297},[],[],[181],{"code":76,"score":22,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[262],[303,304],"参考蒸散发 土壤水分 机器学习","ET0 实际蒸散发 随机森林","参考蒸散发土壤水分机器学习-3804","10.3390\u002Fatmos17100941",{"doi":306,"openalex_id":308,"authors":309,"venue":255,"cited_by_count":97,"oa_url":252,"card":315,"direction":104,"ingested_from":105},"W7214588993",[310,313],{"name":311,"orcid":312},"Halil Karahan","https:\u002F\u002Forcid.org\u002F0000-0001-5346-5686",{"name":314,"orcid":9},"Devrim Alkaya",{"tldr":316,"method":317,"finding":318,"direction":104,"opportunity":319},"评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。","用RF、BT、LSBoost、GAM、MLR结合SHAP、PFI、PDP及SM消","ET0主导预测，缺失时Rs主导且SM补充价值显著增大。","可探索缺ET0条件下融合遥感与土壤湿度的可解释ETa估算框架。","2026-09-29T23:30:57.729880Z",{"id":322,"title":323,"url":324,"summary":325,"summary_zh":326,"content":9,"source_name":327,"source_url":324,"published_at":328,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":329,"score_detail":330,"sources":332,"tags":334,"primary_topic":338,"secondary_topics":339,"classification":345,"search_phrases":364,"slug":367,"view_count":231,"doi":368,"paper":369,"created_at":393},3504,"WaterFormer-MS: A multi-stream deep learning framework for surface water segmentation from satellite imagery","https:\u002F\u002Fdoi.org\u002F10.24850\u002Fj-tyca-18-03-08","Accurate mapping of surface water bodies is essential for dam monitoring, irrigation planning, and sustainable water resource management. Conventional methods based on spectral indices, such as NDWI, can face challenges in complex environments due to spectral ambiguity, seasonal variability, and urban or vegetated surfaces. Deep learning approaches such as U-Net and DeepLabv3+ have improved water-body segmentation; however, accurately delineating fine boundaries and detecting small water bodies remain challenging. To address these limitations, this study proposes WaterFormer-MS, a multi-stream deep learning framework for surface water segmentation from Sentinel-2 imagery. The architecture integrates spectral, geometric, and contextual information through dedicated processing streams and combines the extracted features using a convolutional fusion module enhanced with CBAM attention. This design aims to improve feature representation and support accurate water-body delineation. The proposed model was trained and evaluated using Sentinel-2 imagery from a publicly available Kaggle dataset containing diverse land-cover conditions, including urban, vegetated, and agricultural environments. At 300 epochs, WaterFormer-MS achieved an overall accuracy of 97.4%, a precision of 93.2%, a recall of 94.2%, an F1-score of 93.7%, and an IoU of 89.7%. Within the experimental framework, WaterFormer-MS achieved higher values than the other proposed variants. Comparisons with conventional NDWI-based approaches and published deep learning methods suggest competitive performance; however, these comparisons should be interpreted cautiously because datasets, preprocessing procedures, training protocols, and evaluation settings may differ. The results support the potential of the proposed framework for surface water segmentation and environmental monitoring applications in diverse remote sensing scenarios.","准确绘制地表水体分布对于大坝监测、灌溉规划和水资源可持续管理至关重要。基于NDWI等光谱指数的传统方法在复杂环境中可能因光谱模糊性、季节性变化以及城市或植被覆盖地表而面临挑战。U-Net和DeepLabv3+等深度学习方法改善了水体分割效果，但精确描绘精细边界和检测小型水体仍然具有挑战性。为解决这些局限，本研究提出WaterFormer-MS，一种用于Sentinel-2影像地表水分割的多流深度学习框架。该架构通过专门的处理流整合光谱、几何和上下文信息，并利用经CBAM注意力增强的卷积融合模块将提取的特征进行融合。该设计旨在改善特征表征并支持精确的水体描绘。所提模型使用来自公开Kaggle数据集的Sentinel-2影像进行训练和评估，该数据集包含城市、植被和农业环境等多种土地覆盖条件。在300个训练轮次时，WaterFormer-MS取得了97.4%的总体精度、93.2%的精确率、94.2%的召回率、93.7%的F1分数和89.7%的IoU。在实验框架内，WaterFormer-MS取得了高于其他所提变体的数值。与传统基于NDWI的方法及已发表的深度学习方法进行比较，结果表明其性能具有竞争力；然而，这些比较应谨慎解读，因为数据集、预处理流程、训练方案和评估设置可能存在差异。研究结果支持所提框架在多样化遥感场景中用于地表水分割和环境监测应用的潜力。","Tecnología y Ciencias del Agua","2026-09-24T00:00:00Z",74,{"impact":198,"substance":18,"depth":19,"authority":13,"freshness":52,"relevant":22,"comment":331},"提出多流深度学习框架用于卫星影像地表水提取，精度指标明确，对灌溉规划与水资源监测有参考价值，但属方法类论文，产业影响有限。",[333],{"name":327,"url":324},[182,335,336,29,337],"Sentinel-2","遥感","地表水体提取",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[340,341],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"code":76,"slug":342,"name":343,"path":344,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":46,"locked":14,"version":47,"method":48,"evidence":346},{"candidates":347,"agricultural_evidence":53},[348,354,359],{"code":33,"score":198,"supported":53,"title":349,"abstract":351,"tags":353},[350],"deep learning",[352,350],"深度学习",[182],{"code":40,"score":13,"supported":53,"title":355,"abstract":356,"tags":358},[],[139,61,41,357],"water resource",[29],{"code":76,"score":21,"supported":53,"title":360,"abstract":361,"tags":363},[],[336,362],"remote sensing",[335,336],[365,366],"WaterFormer-MS 地表水 分割","Sentinel-2 水体提取 深度学习","WaterFormer-MS地表水分割-3504","10.24850\u002Fj-tyca-18-03-08",{"doi":368,"openalex_id":370,"authors":371,"venue":327,"cited_by_count":97,"oa_url":324,"card":387,"direction":391,"ingested_from":105},"W7214228825",[372,375,378,381,384],{"name":373,"orcid":374},"Ayoub Benali","https:\u002F\u002Forcid.org\u002F0000-0002-8989-755X",{"name":376,"orcid":377},"Yesma Bendaha","https:\u002F\u002Forcid.org\u002F0000-0002-1347-856X",{"name":379,"orcid":380},"Malika Seddik Bouchouicha","https:\u002F\u002Forcid.org\u002F0009-0001-6092-784X",{"name":382,"orcid":383},"Zakaria Bellahcene","https:\u002F\u002Forcid.org\u002F0000-0003-4353-7871",{"name":385,"orcid":386},"Rachid Rimani","https:\u002F\u002Forcid.org\u002F0000-0002-1923-4312",{"tldr":388,"method":389,"finding":390,"direction":391,"opportunity":392},"提出多流深度学习框架WaterFormer-MS，从Sentinel-2影像中精准分割地表水体。","多流架构融合光谱、几何与上下文特征，CBAM注意力卷积融合，Sentinel-2","300轮训练下总体精度97.4%、IoU 89.7%，优于NDWI及对比深度学习方法。","农业遥感与作物表型","可探索多流框架在农业灌溉水体与小型水塘精细提取中的迁移，并引入时序Sentinel-2提升季节鲁棒性。","2026-09-25T23:30:31.338437Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":406,"tags":408,"primary_topic":411,"secondary_topics":412,"classification":419,"search_phrases":456,"slug":459,"view_count":231,"doi":460,"paper":461,"created_at":478},2926,"An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data","https:\u002F\u002Fdoi.org\u002F10.11591\u002Fijece.v16i5.pp2652-2663","Efficient water management is essential for sustainable agricultural production, particularly in arid and semi-arid regions where water resources are limited. Machine-learning-based irrigation systems can support automated pump-operation decisions using environmental and soil-related sensor data. However, most previous studies have focused on individual machine-learning models, while the application of stacked ensembles to binary irrigation pump-status prediction remains relatively limited. This study proposes a stacking-based framework for predicting irrigation pump operation in an ON\u002FOFF classification setting. The framework uses environmental and soil-related variables, including soil moisture, temperature, humidity, and a numerical time-related feature. Random forest (RF), extreme gradient boosting (XGBoost), and multilayer perceptron models were trained as base learners, and their out-of-fold (OOF) predictions were combined using a logistic-regression meta-learner. The models were evaluated on a held-out test set using accuracy, precision, recall, specificity, and F1-score. The individual models achieved accuracies ranging from 97.34% to 99.95%, with XGBoost providing the best individual performance. The proposed stacking ensemble achieved 99.97% accuracy, 99.94% precision, 100.00% recall, 99.94% specificity, and a 99.97% F1-score. Compared with XGBoost, the ensemble further refined predictive performance, improving accuracy by 0.02 percentage points and F1-score by 0.01 percentage points while achieving complete elimination of false negatives (100.00% recall). These results demonstrate the potential of stacked ensemble learning to improve binary pump-operation prediction and support data-driven irrigation management in water-limited environments.","高效的水资源管理对可持续农业生产至关重要，尤其是在水资源有限的干旱和半干旱地区。基于机器学习的灌溉系统可以利用环境和土壤相关传感器数据支持自动化水泵运行决策。然而，以往大多数研究集中于单一机器学习模型，而堆叠集成（stacked ensemble）在二元灌溉水泵状态预测中的应用仍相对有限。本研究提出了一种基于堆叠（stacking）的框架，用于在开\u002F关（ON\u002FOFF）分类场景下预测灌溉水泵运行状态。该框架使用环境和土壤相关变量，包括土壤湿度、温度、湿度和一个数值型时间相关特征。随机森林（RF）、极端梯度提升（XGBoost）和多层感知机模型被训练为基学习器，其折外（OOF）预测结果通过逻辑回归元学习器进行组合。模型在留出测试集上使用准确率、精确率、召回率、特异度和F1分数进行评估。单一模型的准确率范围为97.34%至99.95%，其中XGBoost的单一模型性能最佳。所提出的堆叠集成达到了99.97%的准确率、99.94%的精确率、100.00%的召回率、99.94%的特异度和99.97%的F1分数。与XGBoost相比，该集成进一步优化了预测性能，准确率提高了0.02个百分点，F1分数提高了0.01个百分点，同时实现了假阴性的完全消除（100.00%召回率）。这些结果表明，堆叠集成学习在改进二元水泵运行预测和支持水资源受限环境下的数据驱动灌溉管理方面具有潜力。","International Journal of Power Electronics and Drive Systems\u002FInternational Journal of Electrical and Computer Engineering","2026-09-18T00:00:00Z",72,{"impact":17,"substance":404,"depth":176,"authority":20,"freshness":52,"relevant":22,"comment":405},21,"提出基于IoT传感器数据的堆叠集成模型实现灌溉水泵开关预测，准确率达99.97%，方法新颖、结论可靠，但属实验室验证阶段，产业影响有限。",[407],{"name":400,"url":397},[181,182,28,409,410],"物联网","智能灌溉",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[413,414],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":415,"slug":416,"name":417,"path":418,"parent_code":37},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":46,"locked":14,"version":47,"method":48,"evidence":420},{"candidates":421,"agricultural_evidence":53},[422,426,431,436,440,446,452],{"code":40,"score":19,"supported":53,"title":423,"abstract":424,"tags":425},[41],[139,61,41],[410],{"code":33,"score":20,"supported":53,"title":427,"abstract":429,"tags":430},[428],"artificial intelligence",[28],[182,28],{"code":415,"score":52,"supported":53,"title":432,"abstract":434,"tags":435},[433],"IoT",[],[409],{"code":212,"score":108,"supported":53,"title":437,"abstract":438,"tags":439},[],[215,216],[],{"code":441,"score":65,"supported":53,"title":442,"abstract":443,"tags":445},"H7",[],[444],"干旱",[],{"code":447,"score":65,"supported":53,"title":448,"abstract":449,"tags":451},"H8",[],[450],"可持续农业",[],{"code":219,"score":22,"supported":14,"title":453,"abstract":454,"tags":455},[],[],[181],[457,458],"智能灌溉 水泵控制 物联网","农业人工智能 智慧农业 智能灌溉 机器学习","智能灌溉水泵控制物联网-2926","10.11591\u002Fijece.v16i5.pp2652-2663",{"doi":460,"openalex_id":462,"authors":463,"venue":400,"cited_by_count":97,"oa_url":397,"card":473,"direction":247,"ingested_from":105},"W7213546497",[464,467,470],{"name":465,"orcid":466},"Sarra Gourari","https:\u002F\u002Forcid.org\u002F0009-0005-1846-0886",{"name":468,"orcid":469},"Wafa Difallah","https:\u002F\u002Forcid.org\u002F0000-0002-6181-0395",{"name":471,"orcid":472},"Belkacem Draoui","https:\u002F\u002Forcid.org\u002F0000-0001-5490-3991",{"tldr":474,"method":475,"finding":476,"direction":247,"opportunity":477},"提出基于堆叠集成学习的灌溉泵ON\u002FOFF预测框架，用物联网传感器数据实现智能灌溉控制。","用RF、XGBoost、MLP作基学习器，逻辑回归元学习器融合OOF预测，基于土","堆叠集成达99.97%准确率和100%召回率，完全消除假阴性，优于单一XGBoost模型。","可探索多模态传感器融合与在线增量学习，提升堆叠集成在不同作物和气候区的泛化与实时部署能力。","2026-09-19T23:30:11.009076Z",{"id":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":9,"source_name":485,"source_url":482,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":487,"sources":489,"tags":491,"primary_topic":493,"secondary_topics":494,"classification":497,"search_phrases":517,"slug":520,"view_count":22,"doi":521,"paper":522,"created_at":540},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",{"impact":20,"substance":404,"depth":176,"authority":20,"freshness":21,"relevant":22,"comment":488},"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[490],{"name":485,"url":482},[181,182,28,492,263],"精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[495,496],{"code":212,"slug":216,"name":268,"path":269,"parent_code":44},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":498},{"candidates":499,"agricultural_evidence":53},[500,504,508,512],{"code":33,"score":121,"supported":53,"title":501,"abstract":502,"tags":503},[55],[28,55],[182,28],{"code":212,"score":198,"supported":53,"title":505,"abstract":506,"tags":507},[216],[215,216],[263],{"code":40,"score":13,"supported":53,"title":509,"abstract":510,"tags":511},[],[139,278,41,276],[492],{"code":219,"score":52,"supported":53,"title":513,"abstract":515,"tags":516},[514],"precision agriculture",[],[181],[518,519],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":521,"openalex_id":523,"authors":524,"venue":485,"cited_by_count":97,"oa_url":9,"card":535,"direction":104,"ingested_from":105},"W7213181547",[525,528,531,533],{"name":526,"orcid":527},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":529,"orcid":530},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":532,"orcid":9},"Kinnoree R. Pasha",{"name":534,"orcid":9},"Dilruba Yeasmin",{"tldr":536,"method":537,"finding":538,"direction":104,"opportunity":539},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z"]