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
Hydropower resources in river basins are typically developed in a cascade manner. The cascade hydropower stations use water from the same river; in a sense, they form a cluster of hydropower stations which are linked ...Hydropower resources in river basins are typically developed in a cascade manner. The cascade hydropower stations use water from the same river; in a sense, they form a cluster of hydropower stations which are linked together by the river stream. The dispatch and management of the cascade hydropower stations in a river basin differ from those of an ordinary single hydropower station. Without doubt, unified dispatch can facilitate the full harnessing of hydraulic resources and is in a better position to fulfill the objectives in the development of river basin. As a result, more and more river-basin cascade power stations around the world implement unif ied dispatching.展开更多
水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域...水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域水质数据为样本,使用线性插值修补缺失数据和剔除的异常数据。使用灰色关联分析计算不同水质指标间的相关性,选择高相关性的水质指标以确定输入变量,并使用门控循环单元(Gated Recurrent Unit, GRU)预测不同的水质指标。将GRA-GRU的预测结果与反向传播神经网络(Back Propagation Neural Network, BPNN)、循环神经网络(Recurrent Neural Network, RNN)、长短期记忆神经网络(Long Short Term Memory, LSTM)、GRU及灰色关联分析-长短期记忆神经网络(Grey Relational Analysis-Long Short Term Memory, GRA-LSTM)进行对比分析,结果显示GRA-GRU在不同水质指标预测上具有较好的适应性,可以有效降低预测误差。其中,与其他模型相比,GRA-GRU预测的化学需氧量在均方根误差上分别降低了3.617%、0.681%、0.478%、1.505%和0.471%。展开更多
随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域...随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域已报道的288篇污染物相关文献,使用多指标综合评分法筛选黄河流域的特征污染物,采用风险商值法获取水样和沉积物中的风险污染物。结果表明:①黄河流域共检出10类144种污染物,采用9类共13个筛选指标构建多指标综合评分法,对污染物各项指标进行评分,然后进行K-means聚类分析,按得分高低分为Ⅰ~Ⅵ级,选取得分较高的33种Ⅰ级和Ⅱ级高分值污染物作为黄河流域特征污染物,包括12种有机氯农药、10种多环芳烃、10种多氯联苯和1种邻苯二甲酸酯。②水样污染物浓度和沉积物含量前5种都是重金属、有机氯农药、邻苯二甲酸酯、多环芳烃以及药品和个人护理产品,而且二者顺序完全一致,且多数污染物的浓度之间存在显著相关性。③根据风险最大化原则,使用风险商值法(RQ)分别对水样和沉积物进行风险评估,将RQ≥0.1的污染物列为风险污染物,水样中共筛选出21种风险污染物,其中RQ≥1的高风险污染物有5种,包括硒、铅、苯并[a,h]蒽、苯并[a]蒽和邻苯二甲酸二丁酯。④沉积物中共筛选出19种风险污染物,其中有13种高风险污染物,包括8种多环芳烃(芘、蒽、荧蒽、苊、萘、芴、苯并[a]蒽、苯并[a,h]蒽)、4种重金属(汞、铅、硒、砷)和1种邻苯二甲酸酯(邻苯二甲酸二丁酯)。该研究对相关部门拟定黄河流域污染物监测方案和管控措施有重要参考意义。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Hydropower resources in river basins are typically developed in a cascade manner. The cascade hydropower stations use water from the same river; in a sense, they form a cluster of hydropower stations which are linked together by the river stream. The dispatch and management of the cascade hydropower stations in a river basin differ from those of an ordinary single hydropower station. Without doubt, unified dispatch can facilitate the full harnessing of hydraulic resources and is in a better position to fulfill the objectives in the development of river basin. As a result, more and more river-basin cascade power stations around the world implement unif ied dispatching.
文摘水质指标具有多元相关性、时序性和非线性的特点,为有效预测河流水质变化,针对水质数据存在缺失和异常的问题,提出基于灰色关联分析-门控循环单元(Grey Relational Analysis-Gated Recurrent Unit, GRA-GRU)的水质预测模型。以淮河流域水质数据为样本,使用线性插值修补缺失数据和剔除的异常数据。使用灰色关联分析计算不同水质指标间的相关性,选择高相关性的水质指标以确定输入变量,并使用门控循环单元(Gated Recurrent Unit, GRU)预测不同的水质指标。将GRA-GRU的预测结果与反向传播神经网络(Back Propagation Neural Network, BPNN)、循环神经网络(Recurrent Neural Network, RNN)、长短期记忆神经网络(Long Short Term Memory, LSTM)、GRU及灰色关联分析-长短期记忆神经网络(Grey Relational Analysis-Long Short Term Memory, GRA-LSTM)进行对比分析,结果显示GRA-GRU在不同水质指标预测上具有较好的适应性,可以有效降低预测误差。其中,与其他模型相比,GRA-GRU预测的化学需氧量在均方根误差上分别降低了3.617%、0.681%、0.478%、1.505%和0.471%。
文摘随着工业化进程的加快和城市化的发展,大量污染物排入黄河流域,并被频繁检出,威胁生态系统和人类健康。为获取潜在生态环境风险污染物,该研究通过调研2000年1月1日−2022年12月31日Web of Science(WoS)和中国知网(CNKI)数据库中黄河流域已报道的288篇污染物相关文献,使用多指标综合评分法筛选黄河流域的特征污染物,采用风险商值法获取水样和沉积物中的风险污染物。结果表明:①黄河流域共检出10类144种污染物,采用9类共13个筛选指标构建多指标综合评分法,对污染物各项指标进行评分,然后进行K-means聚类分析,按得分高低分为Ⅰ~Ⅵ级,选取得分较高的33种Ⅰ级和Ⅱ级高分值污染物作为黄河流域特征污染物,包括12种有机氯农药、10种多环芳烃、10种多氯联苯和1种邻苯二甲酸酯。②水样污染物浓度和沉积物含量前5种都是重金属、有机氯农药、邻苯二甲酸酯、多环芳烃以及药品和个人护理产品,而且二者顺序完全一致,且多数污染物的浓度之间存在显著相关性。③根据风险最大化原则,使用风险商值法(RQ)分别对水样和沉积物进行风险评估,将RQ≥0.1的污染物列为风险污染物,水样中共筛选出21种风险污染物,其中RQ≥1的高风险污染物有5种,包括硒、铅、苯并[a,h]蒽、苯并[a]蒽和邻苯二甲酸二丁酯。④沉积物中共筛选出19种风险污染物,其中有13种高风险污染物,包括8种多环芳烃(芘、蒽、荧蒽、苊、萘、芴、苯并[a]蒽、苯并[a,h]蒽)、4种重金属(汞、铅、硒、砷)和1种邻苯二甲酸酯(邻苯二甲酸二丁酯)。该研究对相关部门拟定黄河流域污染物监测方案和管控措施有重要参考意义。
