Are modern metaheuristics successful in calibrating simple conceptual rainfall–runoff models?

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions.     

Hydrological system analysis and modelling of the Kara River basin (West Africa) using a lumped metric conceptual model

This paper discusses the analysis and modelling of the hydrological system of the basin of the Kara River, a transboundary river in Togo and Benin, as a necessary step towards sustainable water resources management. The methodological approach integrates the use of discharge parameters, flow duration curves and the lumped conceptual model IHACRES. A Sobol sensitivity analysis is performed and the model is calibrated by applying the shuffled complex evolution algorithm. Results show that discharge generation in three nested catchments of the basin is affected by landscape physical characteristics. The IHACRES model adequately simulates the rainfall–runoff dynamics in the basin with a mean modified Nash-Sutcliffe efficiency measure of 0.6. Modelling results indicate that parameters controlling rainfall transformation to effective rainfall are more sensitive than those routing the streamflow. This study provides insights into understanding the catchment’s hydrological system. Nevertheless, further investigations are required to better understand detailed runoff generation processes.

EDITOR M.C. Acreman; ASSOCIATE EDITOR N Verhoest     

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

Hydrologic model development and calibration have continued in most cases to focus only on accurately reproducing streamflows. However, complex models, for example, the so‐called physically based models, possess large degrees of freedom that, if not constrained properly, may lead to poor model performance when used for prediction. We argue that constraining a model to represent streamflow, which is an integrated resultant of many factors across the watershed, is necessary but by no means sufficient to develop a high‐fidelity model. To address this problem, we develop a framework to utilize the Gravity Recovery and Climate Experiment's (GRACE) total water storage anomaly data as a supplement to streamflows for model calibration, in a multiobjective setting. The VARS method (Variogram Analysis of Response Surfaces) for global sensitivity analysis is used to understand the model behaviour with respect to streamflow and GRACE data, and the BORG multiobjective optimization method is applied for model calibration. Two subbasins of the Saskatchewan River Basin in Western Canada are used as a case study. Results show that the developed framework is superior to the conventional approach of calibration only to streamflows, even when multiple streamflow‐based error functions are simultaneously minimized. It is shown that a range of (possibly false) system trajectories in state variable space can lead to similar (acceptable) model responses. This observation has significant implications for land‐surface and hydrologic model development and, if not addressed properly, may undermine the credibility of the model in prediction. The framework effectively constrains the model behaviour (by constraining posterior parameter space) and results in more credible representation of hydrology across the watershed.     

Preliminary Analysis of the Relationship between the Magnitude Deviation of the Station and Site Response in the Fujian Digital Seismic Network

Using the records of 3,069 regional earthquake events from the Fujian Digital Seismic Network from October 2008 to December 2015,in which the magnitude of each of the events was measured by at least six stations,statistics are taken on the deviation between the magnitude of a single station and the average magnitude of the network. It is found that the magnitudes average deviation of each station is-0. 31-0. 68. Statistics are also taken for the period corresponding to the maximum amplitude of the record measured in each station for calculating the magnitude,and the dominant period gained is 0. 06s-0. 38s; site response of each seismic station is inverted using the Moya method,and it is found that the site response of 98 stations is in the bands of 1-20 Hz,suggesting that the site has an amplifying or suppressing effect on the signals in certain frequency bands;Considering the site response corresponding to the inherent 0. 8s period of the WoodAnderson pendulum seismograph,and comparing the magnitude deviation caused by the site response corresponding to the dominant period time of each station with the average magnitude deviation, we obtain that there is a good linear relationship between the magnitude deviation from the dominant period site response and the average deviation of the magnitude of each station,indicating that the magnitude deviation of a single station has a close relationship with the site response of the period corresponding to the maximum amplitude measured for calculating the magnitude.     

Nonlinear FE model updating and reconstruction of the response of an instrumented seismic isolated bridge to the 2010 Maule Chile earthquake

Nonlinear finite element (FE) modeling has been widely used to investigate the effects of seismic isolation on the response of bridges to earthquakes. However, most FE models of seismic isolated bridges (SIB) have used seismic isolator models calibrated from component test data, while the prediction accuracy of nonlinear FE models of SIB is rarely addressed by using data recorded from instrumented bridges. In this paper, the accuracy of a state‐of‐the‐art FE model is studied through nonlinear FE model updating (FEMU) of an existing instrumented SIB, the Marga‐Marga Bridge located in Viña del Mar, Chile. The seismic isolator models are updated in 2 phases: component‐wise and system‐wise FEMU. The isolator model parameters obtained from 23 isolator component tests show large scatter, and poor goodness of fit of the FE‐predicted bridge response to the 2010 Mw 8.8 Maule, Chile Earthquake is obtained when most of those parameter sets are used for the isolator elements of the bridge model. In contrast, good agreement is obtained between the FE‐predicted and measured bridge response when the isolator model parameters are calibrated using the bridge response data recorded during the mega‐earthquake. Nonlinear FEMU is conducted by solving single‐ and multiobjective optimization problems using high‐throughput cloud computing. The updated FE model is then used to reconstruct response quantities not recorded during the earthquake, gaining more insight into the effects of seismic isolation on the response of the bridge during the strong earthquake.     

基于LBS和深度学习的旅游景区客流量的高时频预测

为实现精准的旅游景区客流量的高时频预测,本研究构建了一套基于LBS和深度学习模型的预测方法。此方法可通过对LBS数据的转换实现预测的空间范围与时频控制,并通过方法的核心模型——基于双向循环神经网络和GRU算法构建的深度双向GRU(DBi-GRU)模型完成预测。为检验方法的有效性,研究以深圳大梅沙海滨公园为例对方法进行实验测试。实验使用拟合曲线、误差指标及DM检验3种方法评估DBi-GRU模型的预测效果。此外,实验还设置了其他五种深度学习模型作为DBi-GRU的对照模型,测试基于不同深度学习算法的模型之间的预测水平差异。实验结果表明:(1)本研究提出的DBi-GRU模型在景区客流量高时频预测中具有理想的预测效果,在高峰时段的客流量预测方面也具有较高准确性,预测效果明显优于其他深度学习模型;(2)基于双向循环网络的模型的效果普遍优于基于常规循环网络的模型。尤其是基于双向LSTM算法的模型,虽然预测的准确度略逊色于DBi-GRU模型,但在模型性能上与其的差异并不显著;(3)在相同网络参数下,GRU算法较前人采用的LSTM和RNN算法有着更高的预测准确性。本研究为客流量预测领域的研究提供了一种...     

考虑时间约束的地震救灾数学模型设计与实现

在地震救灾方面,针对信息实时更新的地震救灾数学模型,为了满足不同受灾点的物资需求,增加了出救点数量以及配送时间,以此来保证地震救援物资需求的满足度,提高了应急响应水平。结合地震救灾特点,设计了一种考虑时间约束的地震救灾数学模型。该模型在地震救灾物资需求预测部分,分析影响地震救灾物资需求量的主要因素,构建地震救灾物资需求量计算模型,对影响地震救灾物资需求量的主要因素构建隶属度函数,给出基于模糊综合评判的地震救灾物资需求分级模型。其次,考虑时间约束以及需求不确定等方面,构建考虑时间约束的地震救灾多目标优化数学模型,并给出求解模型。实验结果表明,该模型减少了救灾物资到达受灾点的延迟时间,可在满足时间约束的条件下提高物资需求满足率。     

Ⅶ度抗震设防下牧区居住建筑节能技术研究

对抗震设防下的牧区居民建筑进行节能设计,在提高能源利用率及降低能源消耗方面具有重要意义。传统的建筑节能技术主要通过降低建筑的热能消耗来提高能源利用,存在节能效果差且单一的问题。对此,在Ⅶ度抗震设防下,采用ANSYS软件,引入Block Lanczos法对牧区居住建筑结构的模态进行分析,计算出建筑地震作用,并通过分析牧区居住建筑围护结构及门窗材质来优化建筑节能技术。实验结果表明,在内蒙古牧区居住建筑采用改进提出的建筑节能技术,可有效降低能源消耗,从而能够有效利用能源,使其具有一定的优势。     

考虑不确定性的地震灾害应急救援设备并行优化设计

由于地震灾害的不确定性,使得应急救援设备运行速率及使用效率均受到影响,需要进行并行优化处理。对此,提出基于双向并行计算的地震灾害应急救援设备优化方法。以地震灾区灾情等级评估结果为基础,将地震等级及应急救援设备,设备及设备之间的关系进行标准化处理,转化为求解最优解问题;在考虑不确定性的情况下,通过通信时间与救援设备需求进行双向并行处理,优化地震灾害应急救援设备。实验结果表明,采用改进方法进行地震灾害应急救援设备并行优化,能够对地震灾害应急救援设备需求量进行准确预测,提高应急救援设备的运行速率,缩短通信时间,提高应急救援设备的使用效率,具有一定的优势。     

江西地区地方性震级的量规函数与台基校正值研究

本文采用《江西测震台网地震观测报告》,选取2007年10月~2015年12月所记录的499次M_L≥1.5地震事件,对各子台测定震级与台网平均震级偏差进行定量的统计分析,从震级偏差频次分布、量规函数、台基、方位角等方面分析产生震级偏差的原因。在对量规函数和台基进行校正后,震级偏差绝对值在0.2以内的样本数达到了68.6%,并给出了适合江西地区的量规函数和台基校正值。