水库调度规则是指导水库运行、促进水资源高效利用的重要工具。目前水库调度规则提取方法研究中很少考虑泥沙因素。三峡水库汛期入库泥沙占全年来沙量的90.5%以上,如不考虑进出库泥沙对水库调度的影响,直接将现有方法应用于三峡水库汛...水库调度规则是指导水库运行、促进水资源高效利用的重要工具。目前水库调度规则提取方法研究中很少考虑泥沙因素。三峡水库汛期入库泥沙占全年来沙量的90.5%以上,如不考虑进出库泥沙对水库调度的影响,直接将现有方法应用于三峡水库汛期调度规则提取,其结果难以全面反映汛期三峡水库对干支流来水来沙的调度响应过程。因此,将泥沙因素引入到三峡水库汛期调度规则提取中,以三峡水库汛期历史运行数据和进出库泥沙数据为样本,采用时段末坝前水位和时段平均出库输沙率为联合决策变量,同时考虑长江上游干支流来水来沙及其时滞效应对水库调度的影响,提出一种基于深度学习的水库调度规则提取方法。该方法首先建立RF-LSTM模型,该模型通过随机森林(random forest,RF)算法筛选出对决策变量影响较大的决策因子,并将这些因子作为长短时记忆(long short term memory,LSTM)神经网络的输入变量,进一步识别出决策因子与决策变量之间的映射关系,以实现对水库调度规则的提取与模拟;然后采用自回归方法对模拟精度较差的结果进行误差校正,最后将该方法应用于三峡水库汛期调度规则提取,结果表明:与LSTM模型相比,RF-LSTM模型能有效捕获对决策变量影响较大的决策因子,且具有更高模拟精度及稳定性,其验证期时段末坝前水位和时段平均出库输沙率的NSE值分别为0.995和0.891,分别提高1.7%和1.0%;误差校正后,时段平均出库输沙率的NSE值增至0.944,提高5.6%,模拟精度显著提高。研究结果可为三峡水库汛期调度规则提取提供参考。展开更多
This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independ...This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.展开更多
文摘水库调度规则是指导水库运行、促进水资源高效利用的重要工具。目前水库调度规则提取方法研究中很少考虑泥沙因素。三峡水库汛期入库泥沙占全年来沙量的90.5%以上,如不考虑进出库泥沙对水库调度的影响,直接将现有方法应用于三峡水库汛期调度规则提取,其结果难以全面反映汛期三峡水库对干支流来水来沙的调度响应过程。因此,将泥沙因素引入到三峡水库汛期调度规则提取中,以三峡水库汛期历史运行数据和进出库泥沙数据为样本,采用时段末坝前水位和时段平均出库输沙率为联合决策变量,同时考虑长江上游干支流来水来沙及其时滞效应对水库调度的影响,提出一种基于深度学习的水库调度规则提取方法。该方法首先建立RF-LSTM模型,该模型通过随机森林(random forest,RF)算法筛选出对决策变量影响较大的决策因子,并将这些因子作为长短时记忆(long short term memory,LSTM)神经网络的输入变量,进一步识别出决策因子与决策变量之间的映射关系,以实现对水库调度规则的提取与模拟;然后采用自回归方法对模拟精度较差的结果进行误差校正,最后将该方法应用于三峡水库汛期调度规则提取,结果表明:与LSTM模型相比,RF-LSTM模型能有效捕获对决策变量影响较大的决策因子,且具有更高模拟精度及稳定性,其验证期时段末坝前水位和时段平均出库输沙率的NSE值分别为0.995和0.891,分别提高1.7%和1.0%;误差校正后,时段平均出库输沙率的NSE值增至0.944,提高5.6%,模拟精度显著提高。研究结果可为三峡水库汛期调度规则提取提供参考。
文摘This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.
