Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall co...Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.展开更多
为探究黄河流域复合型灾害的特征及其风险演化模式,首先厘清复合型灾害的基本特征,基于黄河流域2000-2023年的1553条灾害数据,归纳出10种典型的灾害链演化路径。通过构建复合型灾害的复杂网络模型,运用基于节点相似度和标签传播的加权...为探究黄河流域复合型灾害的特征及其风险演化模式,首先厘清复合型灾害的基本特征,基于黄河流域2000-2023年的1553条灾害数据,归纳出10种典型的灾害链演化路径。通过构建复合型灾害的复杂网络模型,运用基于节点相似度和标签传播的加权网络社团划分算法(Weighted Network Community Division Method based on Node Similarity and Label Propagation,SLWCD)对网络节点进行分类,识别影响复合型灾害风险水平的关键节点。结果表明:洪涝灾害为黄河流域复合型灾害网络中的核心节点,具有最强的全局影响力;水污染事故较易受到自然灾害或首发事故的触发,干旱与地震则为黄河流域的高频灾害。聚类分析结果揭示了四类显著的效应机制,分别为:风雨沙灾害与社会安全事件的时空累积效应、各类灾害与公共卫生事件的级联效应、地质灾害与事故灾难的联动效应及土地问题对公共卫生事件的长期影响。此外,通过Python模拟,研究发现黄河流域复合型灾害网络中潜在路径长度大于4的灾害链条共有7646条。基于研究结果,提出了以下政策建议:增强灾害预警与应急响应能力,统筹跨部门协作,强化高风险区域的监测,推进生态保护与可持续发展,优化水资源与污染防控,采取综合适应策略应对气候变化,以有效提升黄河流域应对复合型灾害的能力。展开更多
文摘Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin, a BP neural network model is developed by using observed data, with rainfall conditions serving as affecting factors. The model has satisfactory performance of learning and generalization and can be also used to assess the influence of human activities on water and sediment yield in a river basin. The model is applied to compute the runoff and sediment transmission at Xingshan, Bixi and Shunlixia stations. Comparison between the results from the model and the observed data shows that the model is basically reasonable and reliable.
文摘为探究黄河流域复合型灾害的特征及其风险演化模式,首先厘清复合型灾害的基本特征,基于黄河流域2000-2023年的1553条灾害数据,归纳出10种典型的灾害链演化路径。通过构建复合型灾害的复杂网络模型,运用基于节点相似度和标签传播的加权网络社团划分算法(Weighted Network Community Division Method based on Node Similarity and Label Propagation,SLWCD)对网络节点进行分类,识别影响复合型灾害风险水平的关键节点。结果表明:洪涝灾害为黄河流域复合型灾害网络中的核心节点,具有最强的全局影响力;水污染事故较易受到自然灾害或首发事故的触发,干旱与地震则为黄河流域的高频灾害。聚类分析结果揭示了四类显著的效应机制,分别为:风雨沙灾害与社会安全事件的时空累积效应、各类灾害与公共卫生事件的级联效应、地质灾害与事故灾难的联动效应及土地问题对公共卫生事件的长期影响。此外,通过Python模拟,研究发现黄河流域复合型灾害网络中潜在路径长度大于4的灾害链条共有7646条。基于研究结果,提出了以下政策建议:增强灾害预警与应急响应能力,统筹跨部门协作,强化高风险区域的监测,推进生态保护与可持续发展,优化水资源与污染防控,采取综合适应策略应对气候变化,以有效提升黄河流域应对复合型灾害的能力。
