[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1628":3,"related-1628":84},{"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":22,"classification":23,"search_phrases":53,"slug":54,"view_count":55,"doi":56,"paper":57,"created_at":83},1628,"Water spectrum theory and technology: Supporting water security of National Water Network of China","https:\u002F\u002Fdoi.org\u002F10.1002\u002Frvr2.70070","Abstract The construction of the National Water Network of China (NWNC) is a major national initiative aimed at modernizing water governance and strengthening water security. However, the traditional understanding of water composition and dynamic evolution is not sufficient to support precision management and intelligent regulation of complex water systems. Here, we propose the water spectrum as a new concept and establish its scientific basis, conceptual framework, implementation pathway, and strategic role. The water spectrum is a framework organized around quantity, quality, and state (QQS), designed to support the construction of databases and spectral libraries for all elements of water across different water body types and management scenarios. Enabled by remote sensing, high‐throughput analysis, Internet of Things sensing, and artificial intelligence, it will support dynamic characterization, source tracing, forecasting, and risk identification. As the digital foundation and cognitive engine of the NWNC, the water spectrum will reshape the understanding of water cognition, support intelligent operation, and strengthen national water security.","国家水网建设是旨在推进水治理现代化和强化水安全的重大国家举措。然而，传统上对水组成及其动态演变的理解，尚不足以支撑复杂水系统的精准管理与智能调控。在此，我们提出“水谱”这一新概念，并确立其科学基础、概念框架、实施路径及战略作用。水谱是一个围绕水量、水质和水态（QQS）组织的框架，旨在支持构建涵盖不同水体类型和管理场景的全要素数据库与谱系库。借助遥感、高通量分析、物联网感知及人工智能技术，水谱将支撑动态表征、溯源追踪、预测预报及风险识别。作为国家水网的数字化基础与认知引擎，水谱将重塑对水的认知理解，支撑智能运维，并强化国家水安全。",null,"River","2026-09-02T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为国家水网与水资源安全，非三农或农业信息化核心范畴，故不入选。",[19],{"name":10,"url":6},[21],"智慧水利",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":52},[29,38,45],{"code":30,"score":31,"supported":32,"title":33,"abstract":34,"tags":37},"I3",6,true,[],[35,36],"遥感","remote sensing",[],{"code":39,"score":31,"supported":32,"title":40,"abstract":41,"tags":44},"I5",[],[42,43],"物联网","internet of things",[],{"code":46,"score":47,"supported":32,"title":48,"abstract":49,"tags":51},"I2",3,[],[50],"artificial intelligence",[],"农业相关性需要复核",[],"Waterspectrumtheoryandtechnology:Support-1628",1,"10.1002\u002Frvr2.70070",{"doi":56,"openalex_id":58,"authors":59,"venue":10,"cited_by_count":15,"oa_url":74,"card":75,"direction":81,"ingested_from":82},"W7207608793",[60,63,65,68,71],{"name":61,"orcid":62},"Bo Gao","https:\u002F\u002Forcid.org\u002F0000-0002-7301-6316",{"name":64,"orcid":9},"Siyu Zhu",{"name":66,"orcid":67},"Dongyu Xu","https:\u002F\u002Forcid.org\u002F0000-0002-9001-3635",{"name":69,"orcid":70},"Yifan Zhao","https:\u002F\u002Forcid.org\u002F0000-0003-1011-395X",{"name":72,"orcid":73},"Jianhua Wang","https:\u002F\u002Forcid.org\u002F0000-0002-4048-6055","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Frvr2.70070",{"tldr":76,"method":77,"finding":78,"direction":79,"opportunity":80},"提出水谱概念，支撑中国国家水网的水安全保障。","基于遥感、物联网、AI等技术，构建水谱框架。","水谱作为数字基础和认知引擎，可重塑水认知，支撑智能调控。","其他","水谱概念可迁移至农业水管理，结合遥感与AI，实现农田水要素动态监测与智能决策。","智慧农业 \u002F 农业物联网","openalex","2026-09-04T23:30:18.133978Z",{"total":85,"page":55,"page_size":85,"items":86},4,[87,179,246,335],{"id":88,"title":89,"url":90,"summary":91,"summary_zh":92,"content":9,"source_name":93,"source_url":90,"published_at":94,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":95,"score_detail":96,"sources":103,"tags":105,"primary_topic":110,"secondary_topics":115,"classification":116,"search_phrases":143,"slug":146,"view_count":55,"doi":147,"paper":148,"created_at":178},4915,"Modeling runoff, sediment, and nutrient dynamics in the Ganjiang River Basin, China: A hierarchical SWAT-BiLSTM framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jwpe.2026.111080","Rivers and lakes are vital to regional water security and ecosystem stability, yet the integrated simulation of hydrological, erosion and pollution processes remains constrained by the complex interplay between climate change and human activities. To address systematic biases arising from oversimplified mechanisms and strong non-linearities, this study proposes a hierarchical hybrid framework that sequentially feeds SWAT-simulated hydrological and water-quality processes into a BiLSTM network for residual correction, achieving coordinated, high-precision simulations of runoff, sediment and nutrient fluxes in the Ganjiang River Basin. Validated against multi-source data that hydrological data during 2011–2022 and water quality data during 2021–2022, the SWAT-BiLSTM outperformed standalone SWAT across all metrics, primarily driven by precision gains and residual mitigation rather than computational speedup. Specifically, the average R 2 increased by 6%–9% for runoff, 6%–19% for sediment and significantly by 9%–42% for nutrients, with a maximum P BIAS reduction of 67.4%. The hybrid model further revealed the cascade response mechanisms of the hydrological-erosion-pollution triad, highlighting concentrated rainfall and agricultural cycles as key drivers of seasonal nutrient peaks. These findings confirm the efficacy of deep learning-enhanced mechanistic modeling, offering robust decision support for basin-scale environmental management and pollution control.","河流与湖泊对区域水安全与生态系统稳定至关重要，然而水文、侵蚀与污染过程的集成模拟仍受制于气候变化与人类活动之间复杂的相互作用。为解决机制过度简化与强非线性所导致的系统性偏差，本研究提出了一种分层混合框架，将SWAT模拟的水文与水质过程依次输入BiLSTM网络进行残差校正，从而实现对赣江流域径流、泥沙与营养盐通量的协同高精度模拟。基于多源数据（2011—2022年水文数据及2021—2022年水质数据）的验证表明，SWAT-BiLSTM在所有指标上均优于单一SWAT模型，其优势主要源于精度提升与残差削减，而非计算速度的加快。具体而言，径流平均R²提高了6%—9%，泥沙提高了6%—19%，营养盐显著提高了9%—42%，PBIAS最大降低幅度达67.4%。该混合模型进一步揭示了水文—侵蚀—污染三元体系的级联响应机制，突出表明集中降雨与农业周期是季节性营养盐峰值的关键驱动因素。这些发现证实了深度学习增强机理模型的有效性，为流域尺度环境管理与污染控制提供了有力的决策支持。","Journal of Water Process Engineering","2026-10-13T00:00:00Z",79,{"impact":97,"substance":98,"depth":99,"authority":100,"freshness":101,"relevant":55,"comment":102},16,22,18,14,9,"提出SWAT-BiLSTM分层混合框架，显著提升径流、泥沙与营养盐模拟精度，对流域农业面源污染治理有实质参考价值。",[104],{"name":93,"url":90},[106,107,21,108,109],"深度学习","农业面源污染","水文模型","赣江流域",{"code":46,"slug":111,"name":112,"path":113,"parent_code":114},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":117,"locked":14,"version":25,"method":26,"evidence":118},"classified",{"candidates":119,"agricultural_evidence":32},[120,126,133,138],{"code":46,"score":13,"supported":32,"title":121,"abstract":122,"tags":125},[],[106,123,124],"deep learning","decision support",[106],{"code":127,"score":31,"supported":32,"title":128,"abstract":129,"tags":132},"H7",[],[130,131],"气候变化","climate change",[],{"code":134,"score":55,"supported":14,"title":135,"abstract":136,"tags":137},"H3",[],[],[108],{"code":139,"score":55,"supported":14,"title":140,"abstract":141,"tags":142},"H5",[],[],[107],[144,145],"赣江流域 SWAT-BiLSTM 径流模拟","赣江 泥沙 营养盐 模拟","赣江流域SWAT-BiLSTM径流模拟-4915","10.1016\u002Fj.jwpe.2026.111080",{"doi":147,"openalex_id":149,"authors":150,"venue":93,"cited_by_count":15,"oa_url":90,"card":171,"direction":177,"ingested_from":82},"W7221543472",[151,153,155,157,159,161,163,165,167,169],{"name":152,"orcid":9},"Xihua Wang",{"name":154,"orcid":9},"Xuming Ji",{"name":156,"orcid":9},"Y. Jun Xu",{"name":158,"orcid":9},"Boyang Mao",{"name":160,"orcid":9},"Shunqing Jia",{"name":162,"orcid":9},"Zejun Liu",{"name":164,"orcid":9},"Qinya Lv",{"name":166,"orcid":9},"Chengming Luo",{"name":168,"orcid":9},"Yan Dai",{"name":170,"orcid":9},"Yanxin Rong",{"tldr":172,"method":173,"finding":174,"direction":175,"opportunity":176},"提出SWAT-BiLSTM分层混合框架，高精度模拟赣江流域径流、泥沙与营养物动态。","SWAT模拟水文水质过程，BiLSTM残差校正，用2011-2022年多源数据验","混合模型精度全面优于SWAT，营养物R²提升9%-42%，PBIAS最大降67.4%。","农业绿色发展与碳","可探索深度学习残差校正框架在农业面源污染关键源区识别与减排情景模拟中的推广。","农业人工智能与决策模型","2026-10-14T23:30:55.922882Z",{"id":180,"title":181,"url":182,"summary":183,"summary_zh":184,"content":9,"source_name":185,"source_url":182,"published_at":186,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":187,"score_detail":188,"sources":190,"tags":192,"primary_topic":9,"secondary_topics":197,"classification":198,"search_phrases":230,"slug":233,"view_count":15,"doi":9,"paper":234,"created_at":245},4668,"CONNECTING FLOODWATER, RAINFALL AND RESERVOIR DATA TO INTELLIGENT WATER REDISTRIBUTION FOR DROUGHT-RESILIENT RESOURCE MANAGEMENT - (AQUA RESCUE)","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fview\u002F4739","Many regions of India face floods and droughts in the same year, sometimes within a few hundred kilometres of each other. During a heavy monsoon spell, reservoirs in one basin release large volumes of water to protect their dams, and much of this water flows to the sea, while districts in a neighbouring basin run short of drinking and irrigation water a few months later. The data that could help manage this imbalance, including rainfall records, reservoir storage, river gauge levels and flood extents, is collected by different agencies and is rarely analysed together. This paper presents Aqua Rescue, a data pipeline and decision support system that connects these sources to plan the redistribution of surplus water to drought-prone areas. The pipeline ingests daily rainfall from rain gauge networks and gridded products, reservoir levels and releases from water resources department bulletins, river discharge from gauging stations, groundwater levels from observation wells and flood extent maps derived from satellite radar imagery. Each source is cleaned, converted to common units and mapped to a shared spatial framework of basins, sub-basins, reservoirs and districts. A curated data warehouse keeps both the daily history and the current state of every reservoir and district, and data quality checks catch missing bulletins, unit errors and sudden unrealistic jumps in storage values. Machine learning models use this curated data in three ways. A Long Short-Term Memory network forecasts reservoir inflows for the next fourteen days from recent rainfall, upstream discharge and catchment soil moisture. A gradient boosting model estimates the water demand of each district for drinking, irrigation and industry using population, cropping pattern and temperature data. A drought risk score is computed from the Standardised Precipitation Index, groundwater decline and storage deficit. The forecast surplus and the estimated deficits are then passed to a linear programming optimiser that recommends how much water to move through existing canals, link channels and pumping schemes while respecting capacity, flood cushion and minimum environmental flow constraints.","印度许多地区在同一年内既遭遇洪水又面临干旱，有时两者相距仅几百公里。在季风强降水期间，一个流域的水库为保护大坝而泄放大量水量，其中大部分流入海洋，而邻近流域的各区在几个月后却面临饮用水和灌溉用水短缺。可用于管理这种失衡的数据——包括降雨记录、水库蓄水量、河流水位和洪水范围——由不同机构分别收集，鲜有综合分析。本文提出Aqua Rescue，一个数据管道与决策支持系统，将上述来源连接起来，以规划将盈余水重新分配至易旱地区。该管道摄入来自雨量计网络和格点产品的日降雨量、水资源部门公报中的水库水位与泄放量、水文站的河流流量、观测井的地下水位，以及由卫星雷达影像提取的洪水范围图。每个数据源均经过清洗、转换为统一单位，并映射至流域、子流域、水库和区的共享空间框架。一个经过整理的数据仓库保存每个水库和区的每日历史记录与当前状态，数据质量检查可捕捉公报缺失、单位错误以及蓄水量值的突然非现实跳变。机器学习模型以三种方式使用这些整理后的数据。长短期记忆网络（Long Short-Term Memory network）根据近期降雨、上游流量和流域土壤湿度预报未来十四天的水库入库流量。梯度提升模型（gradient boosting model）利用人口、种植模式和温度数据估算各区的生活、灌溉和工业需水量。干旱风险评分由标准化降水指数（Standardised Precipitation Index）、地下水下降量和蓄水亏缺计算得出。随后，预报盈余量与估算亏缺量被输入线性规划优化器，该优化器在尊重输水能力、防洪库容和最小环境流量约束的前提下，推荐通过现有渠道、连接河道和泵站方案输送多少水量。","International Journal of Engineering Research and Science & Technology","2026-10-08T00:00:00Z",77,{"impact":99,"substance":98,"depth":99,"authority":13,"freshness":101,"relevant":55,"comment":189},"面向印度流域的水数据融合与智能调配决策支持系统，方法链条完整、数据源多元，对农业抗旱与水资源管理有参考价值，但属境外区域性研究，公共影响层级有限。",[191],{"name":185,"url":182},[193,194,21,195,196],"农业遥感","机器学习","水资源调度","抗旱减灾",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":199},{"candidates":200,"agricultural_evidence":14,"reason":52},[201,207,212,219,226],{"code":127,"score":100,"supported":32,"title":202,"abstract":204,"tags":206},[203],"drought",[205,203],"干旱",[],{"code":46,"score":13,"supported":32,"title":208,"abstract":209,"tags":211},[],[194,210,124],"machine learning",[194],{"code":134,"score":101,"supported":32,"title":213,"abstract":214,"tags":218},[],[215,216,217],"灌溉","水资源","irrigation",[],{"code":220,"score":31,"supported":32,"title":221,"abstract":222,"tags":225},"H2",[],[223,224],"土壤","soil",[],{"code":30,"score":55,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[193],[231,232],"Aqua Rescue 水资源调度","印度 洪水 干旱 水库调度","AquaRescue水资源调度-4668",{"doi":9,"openalex_id":235,"authors":236,"venue":185,"cited_by_count":15,"oa_url":239,"card":240,"direction":81,"ingested_from":82},"W7221299254",[237],{"name":238,"orcid":9},"Mr. R. Ramu,  Golla Indu Prasanna,  Mallavalap Divya Reddy,  Boddapati Manjunadh,  Pembarthi Rohith","https:\u002F\u002Fijerst.org\u002Findex.php\u002Fijerst\u002Farticle\u002Fdownload\u002F4739\u002F4377",{"tldr":241,"method":242,"finding":243,"direction":81,"opportunity":244},"构建Aqua Rescue数据管道与决策系统，将洪水、降雨和水库数据整合，优化调水以缓解干旱。","整合多源数据，用LSTM预测入库流量、梯度提升估需水、线性规划优化调水。","系统能连接分散数据，预测盈余与亏缺，在约束下推荐跨区调水方案。","可探索实时物联网数据同化与多目标优化，提升跨流域调水的动态响应与公平性。","2026-10-11T23:30:15.965738Z",{"id":247,"title":248,"url":249,"summary":250,"summary_zh":251,"content":9,"source_name":252,"source_url":249,"published_at":253,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":254,"score_detail":255,"sources":257,"tags":259,"primary_topic":264,"secondary_topics":270,"classification":271,"search_phrases":308,"slug":311,"view_count":55,"doi":312,"paper":313,"created_at":334},2747,"Simulation of the entire process and multi-dimensional sustainability assessment of regional water footprint supply chain using the SCOR-SD-BN coupled framework","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104984","Context Global water resources face critical challenges such as pollution, over-exploitation, and inequitable distribution. This necessitates the development of scientific frameworks to effectively evaluate the sustainability of water resource management. Objective This study establishes a theoretical framework for the agricultural water footprint supply chain, directly elucidates the transformation mechanisms and sustainable evaluation methods from farmland water use to final products, and constructs a system dynamics model for the agricultural water footprint in Heilongjiang Province. This model simulates dynamic processes, enables real-time evaluation of multidimensional sustainability, and analyzes dominant paths. Methods Drawing on the Supply Chain Operations Reference (SCOR) model, this study treated water footprint (WF) and water value as equivalent to the supply chain's total cost and total revenue, respectively. It established a WF supply chain transformation mechanism from water consumption to the product side and used a system dynamics (SD) model to simulate the entire WF supply chain process in Heilongjiang Province, quantitatively analyzing supply chain structure diversity and disturbance amplification. Based on this, a multidimensional evaluation index system covering economic, environmental, and social dimensions was constructed and, in combination with Bayesian Network (BN), fuzzy logic, and semantic mapping, facilitated multidimensional dynamic evaluation and dominant path identification of regional WF supply chain sustainability. Results and conclusions The research findings demonstrate that (1) From 2000 to 2050, both the total cost and total revenue of Heilongjiang Province's WF supply chain consistently exhibit an initial increase followed by a decrease, with the disturbance amplification effect present across all sectoral WF supply chains. (2) In agriculture, apple and beef cattle supply chains possess the strongest sustainability, while soybean and sheep supply chains display the weakest. In the industry, the food manufacturing sector has the strongest sustainability, while the electric heating and metallurgical sectors are the weakest. (3) The comprehensive sustainability index of the WF Supply Chain pursues a distinctly fluctuating upward trajectory from 2000 to 2025. Under the established scenario, the index is projected to continue rising overall from 2026 to 2050, reaching approximately 0.83 around 2045, entering the high sustainability level, and becoming largely stable by 2050. Environmental improvement stands out as the primary strategy when sustainability is low, while economic factors become the principal constraint when sustainability is high. Significance This research establishes a novel analytical framework and quantitative tool for assessing the sustainability of water resource management. The findings decisively guide the promotion of sustainable utilization and management of regional water resources.","背景 全球水资源面临污染、过度开采和分配不均等严峻挑战，亟需构建科学框架以有效评估水资源管理的可持续性。目的 本研究建立农业水足迹供应链的理论框架，直接阐明从农田用水到最终产品的转化机制与可持续评价方法，并构建黑龙江省农业水足迹的系统动力学模型，模拟动态过程，实现多维可持续性的实时评估，并分析主导路径。方法 借鉴供应链运作参考（SCOR）模型，本研究将水足迹（WF）和水价值分别等同于供应链的总成本和总收益，建立了从水消耗到产品端的WF供应链转化机制，并运用系统动力学（SD）模型模拟黑龙江省整个WF供应链过程，定量分析供应链结构多样性和扰动放大效应。在此基础上，构建涵盖经济、环境和社会维度的多维评价指标体系，并结合贝叶斯网络（BN）、模糊逻辑和语义映射，实现区域WF供应链可持续性的多维动态评价和主导路径识别。结果与结论 研究结果表明：（1）2000年至2050年，黑龙江省WF供应链的总成本和总收益均呈现先增后减的趋势，扰动放大效应存在于所有部门WF供应链中。（2）在农业中，苹果和肉牛供应链的可持续性最强，而大豆和绵羊供应链的可持续性最弱。在工业中，食品制造业的可持续性最强，而电热和冶金部门的可持续性最弱。（3）WF供应链的综合可持续性指数在2000年至2025年间呈现明显的波动上升轨迹。在既定情景下，该指数预计在2026年至2050年间总体继续上升，约在2045年达到约0.83，进入高可持续","Agricultural Systems","2026-09-16T00:00:00Z",81,{"impact":99,"substance":98,"depth":99,"authority":100,"freshness":101,"relevant":55,"comment":256},"以SCOR-SD-BN耦合框架模拟黑龙江农业水足迹供应链并做多维可持续评估，方法新颖、数据跨度大，对区域水资源管理有参考价值，但属学术论文，产业落地影响有限。",[258],{"name":252,"url":249},[260,261,21,262,263],"农业供应链","农业水资源","水足迹","可持续评价",{"code":265,"slug":266,"name":267,"path":268,"parent_code":269},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[],{"status":117,"locked":14,"version":25,"method":26,"evidence":272},{"candidates":273,"agricultural_evidence":32},[274,282,288,295,302],{"code":265,"score":101,"supported":32,"title":275,"abstract":276,"tags":281},[],[277,278,279,280],"肉牛","绵羊","cattle","sheep",[],{"code":134,"score":283,"supported":32,"title":284,"abstract":285,"tags":287},7,[],[216,286],"water resource",[261],{"code":289,"score":31,"supported":32,"title":290,"abstract":291,"tags":294},"A4",[],[292,293],"大豆","soybean",[],{"code":296,"score":31,"supported":32,"title":297,"abstract":298,"tags":301},"B2",[],[299,300],"苹果","apple",[],{"code":303,"score":47,"supported":32,"title":304,"abstract":305,"tags":307},"E1",[],[306],"food manufacturing",[],[309,310],"农业供应链 农业水资源 可持续评价 智慧水利","农业供应链 农业水资源","农业供应链农业水资源可持续评价智慧水利-2747","10.1016\u002Fj.agsy.2026.104984",{"doi":312,"openalex_id":314,"authors":315,"venue":252,"cited_by_count":15,"oa_url":249,"card":329,"direction":175,"ingested_from":82},"W7213431804",[316,318,321,323,326],{"name":317,"orcid":9},"He Peng",{"name":319,"orcid":320},"Youzhu Zhao","https:\u002F\u002Forcid.org\u002F0000-0002-2706-8780",{"name":322,"orcid":9},"Meiyun Tao",{"name":324,"orcid":325},"Qiuxiang Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-5790-2334",{"name":327,"orcid":328},"Zilong Wang","https:\u002F\u002Forcid.org\u002F0000-0002-3143-0906",{"tldr":330,"method":331,"finding":332,"direction":175,"opportunity":333},"构建SCOR-SD-BN耦合框架，模拟黑龙江农业水足迹供应链并评估多维可持续性。","SCOR模型、系统动力学、贝叶斯网络、模糊逻辑与语义映射。","水足迹供应链成本收益先增后减，苹果和肉牛链可持续性最强，大豆和羊链最弱。","可将该框架推广至其他区域或作物，结合实时数据实现动态水足迹可持续性预警。","2026-09-17T23:30:04.921468Z",{"id":336,"title":337,"url":338,"summary":339,"summary_zh":340,"content":9,"source_name":341,"source_url":338,"published_at":342,"category":12,"cover_url":9,"hotness":343,"is_selected":14,"score":344,"score_detail":345,"sources":350,"tags":354,"primary_topic":359,"secondary_topics":360,"classification":361,"search_phrases":385,"slug":388,"view_count":389,"doi":390,"paper":391,"created_at":401},2145,"AI in energy and water conservation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22689020","AI-Enabled National Water Conservation and Security System for Qatar A Research and Policy Framework for an Intelligent National Water Digital Twin, Predictive AI and ILM-Based Water Intelligence Platform Prepared as a concept paper for Qatar Government consideration Abstract Qatar’s water security challenge is fundamentally different from that of water-abundant nations. The country operates within an extremely water-stressed environment, with urban water supply heavily dependent on desalination, finite groundwater resources, treated wastewater, extensive distribution infrastructure and increasingly sophisticated storage systems. Qatar has already established significant foundations for addressing this challenge. The Third Qatar National Development Strategy 2024–2030 explicitly identifies sustainable water provision as a national priority, including groundwater protection, sustainable desalination, leak detection, water-balance projects, household conservation and improved agricultural water productivity. Its targets include reducing groundwater extraction by 70%, reducing household water consumption by 33%, bringing per-capita water consumption below 310 litres\u002Fday and reducing water consumption per tonne of crop by 40%. This paper proposes a National AI Water Intelligence Platform (NAWIP): an AI-enabled national decision-support and operational intelligence layer integrating water production, desalination, reservoirs, smart meters, distribution networks, groundwater wells, treated sewage effluent, agriculture, weather, satellite observations, infrastructure assets and behavioural data. The proposed platform combines: Artificial Intelligence and Machine Learning Time-series forecasting Graph Neural Networks Reinforcement Learning Anomaly and leakage detection Computer vision Digital twins Optimization models Geospatial AI Retrieval-Augmented Generation (RAG) Knowledge graphs Agentic AI Arabic language intelligence ILM — an evidence-grounded knowledge and intelligence layer Cryptographic auditability and human governance The objective is not simply to predict water demand. It is to create a national water intelligence capability capable of predicting, explaining, optimizing and coordinating water decisions before water is wasted. 1. Introduction Water is a strategic national resource for Qatar. The challenge is particularly significant because Qatar must simultaneously support: rapidly developing urban areas; residential consumption; commercial activity; industrial production; agriculture and food-security objectives; landscaping and public spaces; emergency reserves; desalination infrastructure; groundwater preservation; treated wastewater reuse; climate resilience. Qatar’s water system therefore needs to be treated as a single interconnected national system rather than a collection of individual utilities. The country already possesses important digital foundations. KAHRAMAA reports a water distribution network exceeding 11,000 km, smart-water-meter deployment and extensive automated water-quality monitoring. It reported 538 million imperial gallons\u002Fday of potable desalinated-water production capacity in 2024. Qatar also has national initiatives through TASMU targeting smart environmental management, including water-consumption reduction and a Smart Water Experience & Insights initiative. The next opportunity is therefore not simply additional infrastructure. It is national intelligence over the infrastructure that already exists. 2. Research Question The central research question is: How can artificial intelligence be deployed at national scale to reduce water consumption, detect water losses, optimize desalination and distribution, preserve groundwater, increase wastewater reuse, improve agricultural water productivity and strengthen Qatar’s long-term water security? A secondary question is: How can an evidence-grounded ILM intelligence layer transform heterogeneous national water data into trustworthy, explainable and auditable decisions for government officials, utilities, regulators, farmers, businesses and citizens? 3. Qatar’s Existing Water-Security Context Qatar’s national strategy already recognizes several critical water challenges. The Third National Development Strategy specifically calls for: groundwater metering; groundwater protection zones; Water Act regulations; limiting groundwater use in fodder cultivation; sustainable desalination technologies such as reverse osmosis; leak-detection technology; water-balance projects; behavioural water conservation; regular monitoring of water quality; improved agricultural water efficiency. The strategy establishes particularly important quantitative targets: National objective 2030 target Per-capita water consumption \u003C310 L\u002Fday Household water consumption −33% Groundwater extraction −70% Water consumption per tonne of crop −40% Renewable energy 4 GW GHG emissions −25% These targets create an unusually strong opportunity for AI because each target can be converted into measurable data, predictive models and automated interventions. 4. The Proposed Solution National AI Water Intelligence Platform — NAWIP The proposed architecture consists of seven intelligence layers. Layer 1 — National Water Data Fabric Integrate: smart water meters; household consumption; commercial consumption; industrial consumption; government facilities; desalination plants; pumping stations; reservoirs; distribution networks; pressure sensors; flow meters; groundwater wells; agricultural irrigation; treated wastewater; rainfall; temperature; humidity; wind; evaporation; satellite imagery; land-use information; crop information; infrastructure maintenance records. The objective is to create a national water data model. 5. National Water Digital Twin The second layer would construct a Digital Twin of Qatar’s water system. Conceptually: Sea → Desalination → Storage → Transmission → Distribution → Customer → Wastewater → Treatment → Reuse alongside: Rainfall → Aquifer → Groundwater → Agriculture → Food production Every important component becomes an entity in the digital twin. For example: Desalination Plant ↓ Transmission Pipeline ↓ Reservoir ↓ District ↓ Pressure Zone ↓ Customer ↓ Consumption The digital twin continuously receives real-world measurements and compares: Expected state vs actual state This enables AI to identify deviations. 6. AI Model Architecture The platform should not depend on one giant AI model. A national water system requires a model ecosystem. 6.1 Demand Forecasting Model Predict: hourly demand; daily demand; weekly demand; seasonal demand; Ramadan demand patterns; extreme-weather demand; district-level demand; household-level consumption patterns. Candidate models: Temporal Fusion Transformer; LSTM; GRU; XGBoost; LightGBM; Prophet for baseline forecasting; ensemble forecasting. Example: D_{t+1}=f(T,H,W,R,P,C,S) where: D = water demand; T = temperature; H = humidity; W = weather; R = rainfall; P = population; C = consumption history; S = seasonal\u002Fevent variables. The model could predict demand at: 15-minute → hourly → daily → monthly → annual horizons. 7. AI Leakage Detection This could become one of the highest-value applications. Instead of waiting for a physical leak to be reported, AI continuously calculates: Expected\\ Flow - Observed\\ Flow If the difference becomes statistically significant, the system investigates. Inputs flow; pressure; pipe age; pipe material; temperature; historical failures; nighttime consumption; customer meter readings; district-metered-area data. Models Isolation Forest; Autoencoders; Bayesian anomaly detection; Graph Neural Networks; change-point detection; physics-informed neural networks. The AI could produce: Potential leakage detected — Zone 17 — probability 94% followed by: Estimated loss: 420 m³\u002FdayProbable location: 1.2 km segmentConfidence: 94%Economic value at risk: XRecommended inspection: Segment A17–A19 This moves Qatar from reactive leak repair to predictive water-loss management. 8. Graph Neural Networks for the Water Network The water network is naturally a graph. Nodes: reservoirs; pumping stations; valves; meters; districts; customers. Edges: pipelines. Therefore, Graph Neural Networks (GNNs)are particularly suitable. A GNN could learn relationships between: Pressure ↓ Flow ↓ Neighbouring pipes ↓ Consumption ↓ Reservoir level This enables the system to distinguish between: normal demand variation and structural anomalies. 9. Groundwater Intelligence Groundwater is strategically important because Qatar identifies it as its only natural water source and has undertaken groundwater monitoring, rehabilitation and artificial recharge initiatives. KAHRAMAA has also been developing an Aquifer Storage Recovery concept using desalinated water for emergency storage. AI can create a National Groundwater Intelligence Model. Inputs: groundwater levels; abstraction; rainfall; geological characteristics; agricultural extraction; salinity; well location; recharge; historical measurements. Models: groundwater-flow models; Physics-Informed Neural Networks; Bayesian models; spatial ML; satellite\u002Fgeospatial models. The system could forecast: Aquifer condition in 30 \u002F 90 \u002F 365 days. It could also identify: Areas where groundwater abstraction is becoming unsustainable. 10. AI for Agriculture Agriculture represents an especially important opportunity because Qatar’s food-security strategy explicitly connects water resources with agricultural productivity. AI could create a Crop Water Intelligence Engine. For every farm: W","AI赋能卡塔尔国家节水与安全系统——面向智能国家水数字孪生、预测性AI及基于ILM的水智能平台的研究与政策框架，作为概念文件供卡塔尔政府审议\n\n摘要\n\n卡塔尔的水安全挑战与水资源丰富国家存在根本性差异。该国处于极度缺水环境中，城市供水高度依赖海水淡化、有限的地下水资源、再生水、庞大的输配基础设施以及日益复杂的储水系统。卡塔尔已在应对这一挑战方面奠定了重要基础。《第三期卡塔尔国家发展战略（2024—2030年）》明确将可持续供水列为国家优先事项，包括地下水保护、可持续海水淡化、漏损检测、水平衡项目、家庭节水及提高农业用水生产率。其目标包括：将地下水开采量减少70%，将家庭用水量减少33%，将人均用水量降至每日310升以下，并将每吨作物用水量减少40%。本文提出建设国家AI水智能平台（NAWIP）：一个AI赋能的国家决策支持与运营智能层，整合水生产、海水淡化、水库、智能水表、配水管网、地下水井、处理后的污水、农业、气象、卫星观测、基础设施资产及行为数据。拟议平台融合：人工智能与机器学习、时间序列预测、图神经网络、强化学习、异常与漏损检测、计算机视觉、数字孪生、优化模型、地理空间AI、检索增强生成（RAG）、知识图谱、代理式AI、阿拉伯语智能、ILM——一个基于证据的知识与智能层、密码学可审计性与人类治理。其目标不仅是预测用水需求，而是构建一种国家水智能能力，能够在水被浪费之前对用水决策进行预测、解释、优化和协调。\n\n1. 引言\n\n水是卡塔尔的战略性国家资源。这一挑战尤为严峻，因为卡塔尔必须同时支撑：快速发展的城市区域；居民用水；商业活动；","Zenodo (CERN European Organization for Nuclear Research)","2026-09-10T00:00:00Z",25,76,{"impact":99,"substance":346,"depth":99,"authority":347,"freshness":348,"relevant":55,"comment":349},20,12,8,"卡塔尔国家级AI水资源智能平台概念论文，方法体系完整且含农业用水效率目标，对智慧水利与农业节水信息化有参考价值，但属他国政策构想、落地性待验证。",[351,352],{"name":341,"url":338},{"name":341,"url":353},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22689019",[355,356,357,21,358],"农业人工智能","数字孪生","水资源管理","农业节水",{"code":46,"slug":111,"name":112,"path":113,"parent_code":114},[],{"status":117,"locked":14,"version":25,"method":26,"evidence":362},{"candidates":363,"agricultural_evidence":32},[364,370,374,380],{"code":46,"score":13,"supported":32,"title":365,"abstract":366,"tags":369},[],[194,367,210,50,368],"计算机视觉","computer vision",[355],{"code":134,"score":283,"supported":32,"title":371,"abstract":372,"tags":373},[],[216,217],[357],{"code":375,"score":283,"supported":32,"title":376,"abstract":377,"tags":379},"I1",[],[356,378],"digital twin",[356],{"code":127,"score":47,"supported":32,"title":381,"abstract":382,"tags":384},[],[383],"climate resilience",[],[386,387],"农业人工智能 水资源管理 农业节水 数字孪生","农业人工智能 水资源管理","农业人工智能水资源管理农业节水数字孪生-2145",2,"10.5281\u002Fzenodo.22689020",{"doi":390,"openalex_id":392,"authors":393,"venue":341,"cited_by_count":15,"oa_url":338,"card":396,"direction":81,"ingested_from":82},"W7212148124",[394],{"name":395,"orcid":9},"Shafiquddin Ahmed Mohammed Riyaz",{"tldr":397,"method":398,"finding":399,"direction":81,"opportunity":400},"提出卡塔尔国家AI水智能平台NAWIP，整合多源数据实现水预测、优化与协调。","融合AI\u002FML、图神经网络、数字孪生、RAG、知识图谱与ILM治理框架。","构建国家水智能能力，可在水浪费前预测、解释、优化和协调水决策。","可延伸至农业用水精准管理，结合卫星与物联网数据优化灌溉和作物水生产力。","2026-09-11T23:30:10.520037Z"]