Water quality monitoring at a virtual watershed monitoring station using a modified deep extreme learning machine

ABSTRACT

A new deep extreme learning machine (ELM) model is developed to predict water temperature and conductivity at a virtual monitoring station. Based on previous research, a modified ELM auto-encoder is developed to extract more robust invariance among the water quality data. A weighted ELM that takes seasonal variation as the basis of weighting is used to predict the actual value of water quality parameters at sites which only have historical data and no longer generate new data. The performance of the proposed model is validated against the monthly data from eight monitoring stations on the Zengwen River, Taiwan (2002–2017). Based on root mean square error, mean absolute error, mean absolute percentage error and correlation coefficient, the experimental results show that the new model is better than the other classical spatial interpolation methods.     

FluBiDi – a model to estimate flood based on runoff: validation using extreme and natural basin conditions

FluBiDi is a two-dimensional model created to simulate real events that can take days and months, as well as short events (minutes or hours) and inclusive laboratory tests. To verify the robustness of FluBiDi, it was tested using a previous study with both designed and real digital elevation models. The results highlight good agreement between the models (i.e. Mike Flood, SOBEK, ISIS 2D, and others) tested and FluBiDi (around 90% for a specific instant and 95% for the complete time simulation). In the simulated hydrographs, the discharge peak value, time to peak, and water level results were accurate, reproducing them with an error of less than 5%. The velocity differences observed in a couple of tests in FluBiDi were associated with very short periods of time (seconds). However, FluBiDi is highly accurate for simulating floods under real topographical conditions with differences of around 2 cm when water depth is around 150 cm. The average water depth and velocities are precise, and the model describes with high accuracy the pattern and extent of floods. FluBiDi has the capability to be adjusted to different types of events and only requires limited input data.     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

基于ShakeMap_CNST的2017年精河6.6级地震灾害快速评估

基于考虑区域地震动衰减关系、场地效应及震中破裂等多因素的烈度快速评估模型,结合震害预测方法,研发了一套震害预测系统,并以2017年8月9日精河M_S6.6地震的震害预测为例,将对其的预测结果与当前主流系统进行对比。结果表明,采用ShakeMap_CNST系统能对地震的影响范围和烈度进行更为准确的估计,在人员伤亡、受灾人口估计、紧急安置人数及经济损失评估等方面,相对于其他系统,本系统的结果与现场调查的结果更为接近。     

基于遗传优化神经网络的高速公路路基沉降量预测

控制路基沉降是公路工程中的一个关键技术问题,而路基沉降与其影响因素之间存在着线性、非线性关系。当输入自变量较多时,用传统神经网络建模容易出现过拟合现象,导致网络模型预测精度较低。针对此问题,本文用遗传算法对神经网络模型的权值和阈值进行优化,同时讨论遗传参数的设定对输出结果的影响。通过对成南高速的实测数据进行仿真,试验结果表明:优化后的BP神经网络具有较高的预测精度,预测效果明显优于传统神经网络模型的输出结果,该预测方法可作为高速公路路基长期沉降预测的一种有效辅助手段。     

BP神经网络算法考虑激活函数后对强震路基塌陷变形预测研究

当前强震后铁路路基变形预测中,相关算法未能考虑激活函数的非线性属性,造成非线性变形特征数据提取不完整,且其特征数据存在偏差,陷入局部最优解。文章提出BP神经网络算法考虑激活函数后对强震路基塌陷变形预测方法,采用双极S因子补偿ReLU非线性激活函数,优化BP神经网络算法,解决非线性路基变形特征数提取问题。利用数据标准化归一方法,对已修正提取的全部变形特征数据进行偏差数据归一,得到路基变形特征数据集合,结合强震后路基变形连接权值计算路基变形预测值,完成强震路基塌陷变形预测。结合实测结果,在matlab下进行预测实验,结果表明所提混合方法可以有效地对水平地震作用下铁路路基塌陷变形程度进行预测,且预测值在误差允许范围内,为铁路的安全运行以及实时维护提供重要依据。     

地震力学成因及前兆机理研究——再“读”唐山地震有感

简述弹性回跳学说、板块构造模型及岩浆活动型地震等地震力学成因的现有观点,并对其进行了讨论。通过分析唐山震区的壳幔深部结构、前兆异常时空演化等资料,进一步论证了唐山地震力学成因为岩浆活动的观点。在此基础上对唐山地震前兆机理进行了研究,指出岩浆活动既是该地震发生的成因,也是各类前兆异常的成因,它们是岩浆活动这枚硬币的两面。最后指出只有正确认识地震和前兆的成因,才有可能用前兆资料对地震三要素做出较好的预报。     

循环荷载作用下红黏土累积变形研究

通过GDS动三轴设备对赣南红黏土进行了一系列排水条件下的大次数循环试验,研究不同循环应力比、固结应力比及围压对赣南红黏土累积变形特性的影响。结果表明：在不同固结应力比、固结压力和循环应力比条件下,红黏土的累积应变都呈现出先迅速累积,后增长变缓,最终稳定的趋势;在一定的循环次数之后,不同初始固结应力和不同初始应力比条件下累积应变与循环次数之间的变形规律呈现为线性关系,且初始固结应力和初始固结应力比越小,其线性相关性越好;不同循环应力比下赣南红黏土的平均累积应变速率与循环次数之间也呈现出明显的线性关系。此外,基于以上关系建立了赣南红黏土在长期循环荷载作用下的累积变形预测模型,并通过对比验证了该模型的可行性。     

基于BIM的地震灾后民用建筑重建工程施工成本控制方法

采用传统的成本控制方法,存在质量和成本无法同时优化、成本预测精确度低,成本控制效果不佳等问题,为此,提出基于BIM的建筑重建工程施工成本控制方法。采用灰色系统理论预测建筑施工成本具体面临的问题,以各期施工成本相关信息数据作为模型数据基础;结合BIM技术进行成本控制动态模型构建,在模型中引入可辨识矩阵设定成本各指标的相对重要度,进一步找寻施工成本各项指标间的关系,按其相互关系进行排序,得到子成本与总成本间的规则联系,并就规则制定控制策略,调整成本控制动态模型以实现施工成本的动态控制。实验结果表明,所提方法的成本预测精度始终在75%以上,成本控制结果与实际值的拟合度高达95%,相比当前方法具有更好的控制性能。     

地震活动性总体参量Rt在北京及邻区地震预测中的应用

采用地震活动性总体参量R_t方法,研究北京及邻区R_t值在中等地震前随时间的变化特征,分析跟踪地震发生前研究区域地震活动状态,探讨中等地震孕震过程的异常信息的变化特征。结果显示:当R_t值大于阈值0.84时,研究区域地震活动状态比较稳定,发生中等地震的可能性较小;反之,表明地震活动处于不稳定状态,具有发生中等地震的危险。检验R_t值的地震预测效能,预测效果比较理想,利用地震活动状态参量R_t研究北京及邻区地震活动状态,判定中等地震发生的风险性,具有一定预测意义。