基于人工神经网络方法的黄河干支流河床演变分析
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摘要
I在过去的50多年里,黄河中、下游的水沙过程发生了显著的变化,二级悬河和主槽萎缩的现象不断加剧,河流的治理面临着巨大的挑战。平滩流量是一个重要的河槽形态特征和过流能力指标,它的确定是黄河干支流治理过程中核心问题之一。潼关位于黄河、洛河、渭河三河汇流区的出口,是黄河的天然卡口,是黄河在晋、陕间南流后东折的拐点,是黄河小北干流、渭河的侵蚀基准面,因此潼关高程的变化一直备受人们的关注。这些问题都是研究黄河干支流河床演变的关键科学问题。本文在前人研究的基础上,系统收集和整理了实测资料,运用河流动力学及河床演变学基本理论,综合分析了黄河下游、渭河下游、黄河内蒙河段平滩流量及潼关高程变化情况以及其影响因素,将平滩流量的影响因子分为三类:①汛期及非汛期来水来沙影响因子;②前期水沙条件的累积作用;③其他因子,例如洪峰流量、泥沙含量、泥沙粒径等。潼关高程的影响因子分为四类:①来水来沙影响因子;②三门峡水库的影响;③前期水沙条件的累积作用;④其他因子,例如洪峰流量、泥沙粒径等。人工神经网络方法是一种研究复杂非线性问题十分有效的手段,本文运用人工神经网络方法,分别建立了黄河下游花园口、高村、艾山、利津断面、渭河下游华县站、黄河内蒙河段三湖河口站各影响因子与平滩流量的BP模型,以及潼关高程与其影响因子的BP模型。通过对模型结果分析得出以下结论:
(1)汛期及非汛期来水来沙影响因子均对平滩流量具有较大影响,且非汛期来水来沙影响因子不可忽略。
(2)滞后响应是冲积河流河床演变的一种普遍现象。对于平滩流量的计算,使用人工神经网络方法直接输入前n年水沙因子能够更好的反映累积影响;对于潼关高程的计算,滑动平均方法体现水库运用方式的累积影响效果精度更高。
(3)考虑其他因子如洪峰流量、泥沙含量、泥沙粒径等有利于平滩流量计算精度的提高。潼关站和华县站洪水期日平均流量大于2500m~3/s持续的天数与潼关高程相关性最好,训练和检验的平均误差均最小。对于非汛期水库运用方式对潼关高程的影响则是将非汛期坝前水位高于315m、320m和322m分别持续的天数共同作用时相关性最好。
In the past fifty years, the water and sediment process has changed a lot in the Lower Yellow River, resulting in secondary suspended river and main channel shrinkage, which causes great challenge to the river management. The bankfull discharge is significant for potential discharging capacity of the main channel to pass flood flows and to transport sediment. It is one of the core issues to identify the bankfull discharge in the process of governance of the Yellow River. Tongguan is the convergence exports in the Yellow River and Luo River and Wei River, is a natural block port of the Yellow River, is the inflection point of Yellow River in Shanxi- Shaanxi Arear, is erosion base level of North Yellow River and Weihe River. So Tongguan Elevation change has always been concerned. These issues are all key scientific issues in fluvial processes of Middle and Lower Reaches of the Yellow River. In this paper, based on previous studies the author detailedly collected and measured data. The changes and influence factors of Tonguan elevation and bankfull discharge of Lower Yellow River、Lower Weihe River and Inner Mongolia of Yellow River was analysed by using of basic theory of fluvial dynamics and basic principle of fluvial processes. The influence factors of bankfull discharge were divided into there types:①the impact factor of water flow and incoming sediment in flood and non-flood season;②the accumulative effect of several consecutive years flow discharge and sediment load conditions;③other factors like peak discharge、sediment content, etc. The influence factors of Tonguan elevation were divided into four types:①the impact factor of water flow and incoming sediment;②the influence of Three Gorges dam builded;③the accumulative effect of several consecutive years flow discharge and sediment load conditions;④other factors like peak discharge、sediment content, etc. Artificial neural network is a very effective means to soule complex nonlinear problem. By Artificial neural network, BP models of bankfull discharge and impact factor of bankfull discharge were established in Huayuankou, Gaocun, Aishan, Lijin, Huaxian, Sanhuhekou stations. At the same time BP models of Tonguan elevation and impact factor of Tonguan elevation were established. Through the analysis of model results, the following conclusions was obtained:
(1) the impact factor of water flow and incoming sediment in non-flood season can not be ignored. Flood and non-flood water and sediment impact factors are all important to bankfull discharge.
(2) The delayed response is a universal phenomenon in the evolution of alluvial river channel. For bankfull discharge calculation, BP method input the data for the past n years can reflect the accumulative effect better. For Tonguan elevation calculation, using Moving average method can get more accurate.
(3) Consider other factors such as peak discharge, sediment concentration, sediment particle size is conducive to bankfull discharge increased accuracy. They are the best correlation between Tonguan elevation and the number of days of daily mean flow greater than 2500m~3/s in flood season at Tonguan and Huaxian station. For inflence of operational mode of Three Gorges dam in non-flood season to Tonguan elevation, it has best correlation between Tonguan elevation and the number of days of dam front water level higher than the 315m, 320m and 322m.
(1)汛期及非汛期来水来沙影响因子均对平滩流量具有较大影响,且非汛期来水来沙影响因子不可忽略。
(2)滞后响应是冲积河流河床演变的一种普遍现象。对于平滩流量的计算,使用人工神经网络方法直接输入前n年水沙因子能够更好的反映累积影响;对于潼关高程的计算,滑动平均方法体现水库运用方式的累积影响效果精度更高。
(3)考虑其他因子如洪峰流量、泥沙含量、泥沙粒径等有利于平滩流量计算精度的提高。潼关站和华县站洪水期日平均流量大于2500m~3/s持续的天数与潼关高程相关性最好,训练和检验的平均误差均最小。对于非汛期水库运用方式对潼关高程的影响则是将非汛期坝前水位高于315m、320m和322m分别持续的天数共同作用时相关性最好。
In the past fifty years, the water and sediment process has changed a lot in the Lower Yellow River, resulting in secondary suspended river and main channel shrinkage, which causes great challenge to the river management. The bankfull discharge is significant for potential discharging capacity of the main channel to pass flood flows and to transport sediment. It is one of the core issues to identify the bankfull discharge in the process of governance of the Yellow River. Tongguan is the convergence exports in the Yellow River and Luo River and Wei River, is a natural block port of the Yellow River, is the inflection point of Yellow River in Shanxi- Shaanxi Arear, is erosion base level of North Yellow River and Weihe River. So Tongguan Elevation change has always been concerned. These issues are all key scientific issues in fluvial processes of Middle and Lower Reaches of the Yellow River. In this paper, based on previous studies the author detailedly collected and measured data. The changes and influence factors of Tonguan elevation and bankfull discharge of Lower Yellow River、Lower Weihe River and Inner Mongolia of Yellow River was analysed by using of basic theory of fluvial dynamics and basic principle of fluvial processes. The influence factors of bankfull discharge were divided into there types:①the impact factor of water flow and incoming sediment in flood and non-flood season;②the accumulative effect of several consecutive years flow discharge and sediment load conditions;③other factors like peak discharge、sediment content, etc. The influence factors of Tonguan elevation were divided into four types:①the impact factor of water flow and incoming sediment;②the influence of Three Gorges dam builded;③the accumulative effect of several consecutive years flow discharge and sediment load conditions;④other factors like peak discharge、sediment content, etc. Artificial neural network is a very effective means to soule complex nonlinear problem. By Artificial neural network, BP models of bankfull discharge and impact factor of bankfull discharge were established in Huayuankou, Gaocun, Aishan, Lijin, Huaxian, Sanhuhekou stations. At the same time BP models of Tonguan elevation and impact factor of Tonguan elevation were established. Through the analysis of model results, the following conclusions was obtained:
(1) the impact factor of water flow and incoming sediment in non-flood season can not be ignored. Flood and non-flood water and sediment impact factors are all important to bankfull discharge.
(2) The delayed response is a universal phenomenon in the evolution of alluvial river channel. For bankfull discharge calculation, BP method input the data for the past n years can reflect the accumulative effect better. For Tonguan elevation calculation, using Moving average method can get more accurate.
(3) Consider other factors such as peak discharge, sediment concentration, sediment particle size is conducive to bankfull discharge increased accuracy. They are the best correlation between Tonguan elevation and the number of days of daily mean flow greater than 2500m~3/s in flood season at Tonguan and Huaxian station. For inflence of operational mode of Three Gorges dam in non-flood season to Tonguan elevation, it has best correlation between Tonguan elevation and the number of days of dam front water level higher than the 315m, 320m and 322m.
引文
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