KALMAN FILTER ESTIMATION AND PREDICTION OF DAILY STREAM FLOWS: I. REVIEW,ALGORITHM, AND SIMULATION EXPERIMENTS1

An important class of models, frequently used in hydrology for the forecasting of hydrologic variables one or more time periods ahead, or for the generation of synthetic data sequences, is the class of autoregressive(AR) models. As the AR models belong to the family of linear stochastic difference equations, they have both a deterministic and a stochastic component. The stochastic component is often assumed to have a Gaussian distribution. It is well known that hydrologic observations (e.g., stream flows) are heavily affected by noise. To account explicitly for the observation noise, the linear stochastic difference equation is expressed in state variable form and an observation model is introduced. The discrete Kalman filter algorithm can then be used to obtain estimates of the state variable vector. Typically, in hydrologic systems, model parameters, system noise statistics and measurement noise statistics are unknown, and have to be estimated. In this study an adaptive algorithm is discussed which estimates these quantities simultaneously with the state variables. The performance of the algorithm is evaluated by using simulated data.     

FLOOD-RUNOFF FORECASTING WITH HEC1F1

HEC1F is a computer program for making short- to medium-term forecasts of uncontrolled flood runoff. The program employs unit hydrographs and hydrologic routing to simulate runoff from a subdivided basin. Estimates of future rainfall can be accommodated. Runoff parameters for gaged headwater subbasins can be estimated (optimized) in real time. Blending of calculated with observed hydrographs can be performed. HEC1F is a component of an on-line software system that includes capability for data acquisition and processing, precipitation analysis, streamflow forecasting, reservoir system analysis, and graphical display of data and simulation results. The conceptual framework for HEC1F is described, and application of the program is illustrated.     

FORECAST UNCERTAINTY IN WATER SUPPLY RESERVOIR OPERATION1

ABSTRACT: This research investigates the benefits of forecasting in water supply systems. Questions relating operational losses to forecast period and accuracy are addressed. Some simple available forecasting techniques are assessed for their accuracy and applicability. These issues are addressed through the use of a simulation model of the Cedar and South Fork Tolt Rivers, where the system is modeled as a single purpose reservoir supplying municipal and industrial water to the Seattle metropolitan area. The following conclusions were made for this system: (1) reservoir operation deteriorates markedly with the loss of forecast accuracy; (2) the optimal length of forecasting period is five months; (3) reservoir operation may be improved by as much as 88 percent if perfect predictive abilities are available; (4) the mean of the historic data is not recommended to predict future flows because Markov methods are always superior; and (5) lag-one autoregressive Markov schemes exhibit about a 9 percent improvement in operation over no forecasting.     

我国重大活动环境空气质量监测预报评估技术体系的发展与思考

通过总结南京青年奥林匹克运动会、二十国集团领导人峰会、金砖国家领导人第九次会晤、中国国际进口博览会、第七届世界军人运动会等重大活动环境空气质量保障工作经验,简述近年来中国重大活动环境空气质量监测预报评估技术的进展以及所发挥的作用。结合城市环境精细化管控和未来我国"十四五"环境空气质量持续改进的需求,提出在监测网络、监测技术、排放清单、预测预报产品和保障机制等方面有待提升的建议。     

用灰色系统理论对新疆主要河流水质进行预测和分析

在对新疆河流水质现状进行分析评价的基础上,用GM(1,1)模型对今后五年内的河流水质进行了预测。     

环境预测中的GAM模型

当原始序列属代数曲线型时，应用ＧＡＭ模型可以提高精度，从而避免进行ＧＭ（１，１）模型的残差修正二次建模。从分析ＧＭ（１，１）模型的缺陷着手，结合实例详细介绍了ＧＡＭ模型的思想形成和计算方法。     

A REVIEW OF APPLICATION ISSUES OF THE METROPOLITAN WATER DISTRICT-MAIN WATER FORECASTING SYSTEM1

ABSTRACT: This paper reviews the processes that occurred during an application of the Metropolitan Water District (MWD)-MAIN water use forecasting system for the City of Salinas, California. The review includes an analysis of sources of available data, methods for estimating input data, calibration, and verification of the MWD-MAIN System, and an evaluation of the reliability of system output. We found that inexperienced users can have difficulty understanding the level of skill, knowledge, and amount of data that are required to produce reliable forecasts. Some of the issues associated with application of the MWD-MAIN System include the following:

• ? All input data needed for accurate forecasts simply are not available for many cities and towns.
• ? The data requirements are more extensive than many users anticipate.
• ? Substantial requirements for manipulation of input data produces opportunity for error that creates major time demands in troubleshooting.
• ? Calibration and verification for specific uses can be substantially more difficult than is readily apparent from the guidance manual.
• ? Independent validity checks need to be done to validate system output.
• ? If specified calibrating procedures do not produce reasonable results, reestimating slope coefficients is an option, but this requires resources and expertise that can easily exceed the limits of most users.

These are problems typical of most complex models. Reviews such as this can help users to appreciate the level of data required, and to use the MWD-MAIN System in a more effective and efficient manner.     

高邮地磁台加卸载响应比与江苏及邻近地区的地震预测

研究分析了高邮地磁台1980～2001年观测资料的加卸载响应比异常与江苏及邻近地区ML5.0以上地震之间的关系,提出了适用于高邮地磁台的地震分析预报参数,并对其内检预报效能进行了评价。     

灾变预测在火灾预测中的应用

简述了灰色理论中灾变预测的特点及其建模过程，并将其应用于湖南省和山东省火灾预测之中。经检验，模型的精度等级为好，模型的预测结果可靠，对于消防部门火灾的预测有一定帮助。     

网络路由信息安全应用研究——基于流量预测的路由选择新算法

根据灰色系统的预测理论 ,笔者提出了一种关于网络数据流量变化发展的预测方法 ,并为其建立了基于网络流量变化趋势的预测模型。依据该模型对网络中各结点未来流量的预测结果 ,可用实时动态来确定最佳的路由选择策略 ,从而在根本上解决网络拥塞现象的发生 ,大大地提高了网络数据通信时的安全可靠性。