River flow forecasting for multiple time periods

1988 ◽  
Vol 15 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Donald H. Burn
Keyword(s):  
Kalman Filtering ◽  
River Flow ◽  
Forecast Accuracy ◽  
Forecasting Model ◽  
Lead Times ◽  
Multiple Time ◽  
Filtering Algorithm ◽  
River Flow Forecasting ◽  
Forecast Precision ◽  
Time Periods

The performance of a river flow forecasting model employing a Kalman filtering algorithm was evaluated for increasing forecast lead times. The expected decrease in forecast accuracy was quantified and a decrease in forecast precision was noted for increased lead times. The merits of external estimates of meteorological inputs to the model were evaluated through an examination of different forecasting options. It was revealed that even noisy estimates of meteorological events improved the flow forecasts. Key words: forecasting, Kalman filter, real time, precipitation, snowmelt.

River Flow Forecasting Model For Sturgeon River

1985 ◽  
Vol 111 (2) ◽  
pp. 316-333 ◽  
Author(s):  
Donald H. Burn ◽  
Edward A. McBean
Keyword(s):  
River Flow ◽  
Forecasting Model ◽  
River Flow Forecasting

scholarly journals Improved higher lead time river flow forecasts using sequential neural network with error updating

2014 ◽  
Vol 62 (1) ◽  
pp. 60-74 ◽  
Author(s):  
Om Prakash ◽  
K.P. Sudheer ◽  
K. Srinivasan
Keyword(s):  
Neural Network ◽  
Lead Time ◽  
River Flow ◽  
Forecast Accuracy ◽  
Flood Management ◽  
Residual Variance ◽  
Case Examples ◽  
River Flow Forecasting ◽  
Forecast Lead Time ◽  
Artificial Neural Network Ann

Abstract This paper presents a novel framework to use artificial neural network (ANN) for accurate forecasting of river flows at higher lead times. The proposed model, termed as sequential ANN (SANN), is based on the heuristic that a mechanism that provides an accurate representation of physical condition of the basin at the time of forecast, in terms of input information to ANNs at higher lead time, helps improve the forecast accuracy. In SANN, a series of ANNs are connected sequentially to extend the lead time of forecast, each of them taking a forecast value from an immediate preceding network as input. The output of each network is modified by adding an expected value of error so that the residual variance of the forecast series is minimized. The applicability of SANN in hydrological forecasting is illustrated through three case examples: a hypothetical time series, daily river flow forecasting of Kentucky River, USA and hourly river flow forecasting of Kolar River, India. The results demonstrate that SANN is capable of providing accurate forecasts up to 8 steps ahead. A very close fit (>94% efficiency) was obtained between computed and observed flows up to 1 hour in advance for all the cases, and the deterioration in fit was not significant as the forecast lead time increased (92% at 8 steps ahead). The results show that SANN performs much better than traditional ANN models in extending the forecast lead time, suggesting that it can be effectively employed in developing flood management measures.

Development and Application of a Storage Model for River Flow Forecasting

1983 ◽  
Vol 14 (3) ◽  
pp. 139-154 ◽  
Author(s):  
Takeshi Hata ◽  
Malcolm G. Anderson
Keyword(s):  
River Flow ◽  
Spatial Scales ◽  
Forecasting Model ◽  
Data Input ◽  
Storage Model ◽  
Topographic Data ◽  
River Flow Forecasting ◽  
Wide Range ◽  
Temporal And Spatial

A lumped sequential river flow forecasting model is outlined. It is shown to be flexible in both temporal and spatial scales, thereby allowing simulations to be undertaken for a wide range of practical purposes. In addition, the required data input is very low, and is restricted to topographic data for only small segments of the entire catchment. The model is successfully applied to the River Avon in England and the River Kako in Japan.

Transmission rate conditions for distributed filtering in sensor networks against eavesdropper

2021 ◽  
pp. 014233122110056
Author(s):  
Bingya Zhao ◽  
Ya Zhang
Keyword(s):  
Sensor Networks ◽  
Topological Entropy ◽  
Kalman Filtering ◽  
Transmission Rate ◽  
Necessary Condition ◽  
Estimation Errors ◽  
Positive Role ◽  
Filtering Algorithm ◽  
Wiretap Channels ◽  
Kalman Filtering Algorithm

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.

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