Real-time flood forecasting with the use of inadequate data

Abstract

A modelling scheme is developed for real-time flood forecasting. It is composed of (a) a rainfall forecasting model, (b) a conceptual rainfall-runoff model, and (c) a stochastic error model of the ARMA family for forecast error correction. Initialization of the rainfall-runoff model is based on running this model on a daily basis for a certain period prior to the flood onset while parameters of the error model are updated through the Recursive Least Squares algorithm. The scheme is suitable for the early stages of operation of flood forecasting systems in the presence of inadequate historical data. A validation framework is set up which simulates real-time flood forecasting conditions. Thus, the effects of the procedures for rainfall-runoff model initialization, forecast error correction and rainfall forecasting are assessed. Two well-known conceptual rainfall-runoff models (the Soil Moisture Accounting model of the US National Weather Service River Forecast Service—SMA-NWSRFS and TANK) together with data from a Greek basin are used for illustration purposes.     

基于概率神经网络的智能找矿方法——以四川雅江县木绒锂矿为例

概率神经网络是一种分类准确率高、泛用性强、可以包容一定数量错误样本的人工神经网络,极其适合勘查地球化学找矿中的预测找矿靶区。本文以四川雅江县木绒锂矿为例,运用概率神经网络搭建智能找矿模型,以已知区的Li元素及与其相关性强的Rb-Cs-Al-Fe元素作为训练指标,对模型进行训练,经过多次训练后将Spread值确定为0.08,使模型在训练集和测试集的准确率均大于80%,实现非线性的指标与成矿潜力的对应,得到本矿区的PNN模型,然后对预测区的样本数据进行预测,成功圈定了1处靶区。为检验靶区准确性,以Li、Rb、Cs元素数据累计频率的80%作为异常下限,圈出的异常区域与靶区位置基本重叠。对预测区进行了实地查证工作,发现两条红柱石带,其中一条与靶区位置吻合,表明该神经网络模型准确性高,可用于矿产勘查的预测研究。     

定向移动基于马尔科夫链的时空不确定性

概率时间地理是经典时间地理基于概率的一种扩展,它采用概率描述移动对象在可达位置的非等可能性。已有的概率时间地理是基于正态分布或布朗桥的,其方差与移动速度无关或随移动速度的增大而发散,因而难以兼顾应用针对性和稳定性。本文提出了一种基于马尔科夫链的概率时间地理方法。首先,构建中间关于两边的双向条件马尔科夫链,它在移动速度足够大时的极限可视为布朗桥,因而具有稳定性数字特征。然后,建立定向移动到马尔科夫链的映射关系,主要是根据定向移动的时空位置、移动速度等信息建立马尔科夫链的步长、状态空间和转移矩阵,这样马尔科夫链与移动速度有关。最后,利用双向马尔科夫链连续计算定向移动在任意时刻的概率分布云,其方差的针对性和稳定性在实例中进行了验证。     

利用概率松弛法的城市路网自动匹配

多源空间数据匹配是空间数据集成与互操作,变化检测与数据更新的重要前提。路网数据匹配在导航、智能交通和基于位置服务等领域具有重要的研究意义和实用价值。本文提出一种基于概率松弛方法的城市路网自动匹配方法,该方法首先通过路段间几何差异性估算候选路段的初始概率,然后根据邻接候选匹配路段的兼容性不断更新原概率矩阵直到收敛于某一极小值。最后基于收敛的概率矩阵计算各候选路段的结构相似性,并通过设定相应的规则选取和提炼1: 1, 1: M和M: N匹配对。实验选取中国武汉,瑞士苏黎世地区的OpenStreetMap数据与导航数据进行匹配算法的验证。结果表明：本文算法对非刚性偏差较大的路网数据能达到较高精度,不存在匹配方向性问题,且能够识别1: 0, 1: M和M: N匹配。     

基于天气预报的参考作物腾发量LS-SVM预测模型

利用最小二乘支持向量机(LS-SVM)方法,建立了基于天气预报的参考作物腾发量(ET₀)的预测模型.对广利灌区1997~2006年逐日气象信息中的天气类型和风速等级进行量化后,以不同天气预报信息作为输入量,建立10种验证方案,对2007年的逐日ET₀进行预测.经验证,方案1~方案7精度均令人满意,其中方案1精度最高.方案1的输入量为气温、天气类型、风速等级3项的预测值,该方案的模型预测值与计算值的统计参数分别为:均方根偏差E_RMS为0.5182 mm,相对偏差E_R为0.1878,决定系数R2为0.864 8,认同系数I_A为0.966 9,回归系数R_C为0.9867;方案7精度亦较好,且以上指标统计参数依次为0.6576 mm、0.2332、0.986 6、0.774 7及0.986 6,该方案输入量只有气温项,实用性很强.     

利用BP神经网络提高奥运会空气质量实时预报系统预报效果

将BP(Back Propagation)神经网络方法引入到奥运空气质量预报工作中,利用MATLAB神经网络工具箱搭建运行平台,将高时效性的观测结果与多模式集成实时预报系统的模式输出结果相结合,做出BP神经网络拟合预报结果。在对北京大学医学部站点2008年7月7日到8月26日模式模拟结果、观测结果以及BP神经网络拟合结果的对比研究中发现:BP神经网络能大大提高模式预报效果,平均误差率减少34.7%,相关系数提高39%,特别是在模式模拟效果较差的情况下,对提高预报效果更明显。对BP神经网络样本问题进行敏感性实验结果表明,样本数目多少并不是决定拟合效果的决定性因素,应选取具有稳定映射关系的样本,才是提高拟合预报效果的关键。     

Application of the Takagi–Sugeno fuzzy system for combination forecasting of river flow in semiarid mountain regions

Because of high spatial heterogeneity and the degree of uncertainty about hydrological processes in large‐scale catchments of semiarid mountain areas, satisfactory forecasting of daily discharge is seldom available using a single model in many practical cases. In this paper the Takagi–Sugeno fuzzy system (TS) and the simple average method (SAM) are applied to combine forecasts of three individual models, namely, the simple linear model (SLM), the seasonally based linear perturbation model (LPM) and the nearest neighbour linear perturbation model (NNLPM) for modelling daily discharge, and the performance of modelling results is compared in five catchments of semiarid areas. It is found that the TS combination model gives good predictions. The results confirm that better prediction accuracy can be obtained by combining the forecasts of different models with the Takagi–Sugeno fuzzy system in semi‐arid mountain areas. Copyright © 2009 John Wiley & Sons, Ltd.     

1993年LAFS数值降水预报产品评价

应用天气学和统计学方法对1993年LAFS数值降水预报产品进行检验。结果表明:多数主要降水过程预报较好；全年降水10mm级和25mm级降水预报准确率高于50mm级降水的准确率；南方降水预报水平比其它地区高，高原地区最差；降水预报范围过大是LAFS的一大弱点。     

Probabilistic assessment of seismic hazard of dam sites in Jordan

A Probabilistic method is used to evaluate the seismic hazard of nineteen embankment dam sites in Jordan. A line source model developed by McGuire (1978) is used in this study. An updated earthquake catalogue covering the period from 1 A.D. to 1991 A.D. is used for this purpose. This catalogue includes all earthquakes that occurred in Jordan and adjacent areas, more specifically between latitudes 27.0°–35.5° N and longitudes 32.0°–39.0° E.Nine distinct seismic sources of potential seismic activities are identified. The seismic hazard parameters are determined using the method suggested by Kijko and Sellevoll (1989).The Peak Ground Acceleration (PGA) is selected as a measure of ground motion severity. Esteva (1974) attenuation relationship is used in evaluating PGA values at each dam site. Analysis is carried out for 50%, 90%, and 95% probability that is not being exceeded in a life time of 50, 100, and 200 years.Results of analysis indicate that PGA values are higher for dam sites closer to the Dead Sea Fault. This fault is believed to be responsible for most earthquake activities in Jordan and vicinity. The highest PGA value is found to be for Al-Karama dam site.     

A note on the performance of climatic forecasts for Asia based on the periodic portion of the data: 1986–1987

Between 1973 and 1986 a group at the University of Wisconsin worked on the use of the periodic portion of climatic time series with the aim of exploring the potential for year-or-more in advance forecasting. This paper reports on the real time verification of the last sets of forecasts made by the group. From spectra of temperature and cube-rooted precipitation the dominant frequencies were chosen. These were usually related to tidal frequencies. A Fourier series of these dominant terms was then fitted to the dependent data set and future values calculated. These were analyzed for forecast skill, and the skillful Fourier series retained. Real time forecasts were then made. Verification shows a low probability that the forecast skills were obtained by chance. It is suggested that the periodic term might be a useful addition to more standard approaches to long range forecasting.