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

Title:

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

Creator:

Prakash, Om, Sudheer, K.P., and Srinivasan, K.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7965d88e-085f-4fc6-bde1-f1333d29b54d
uuid:7965d88e-085f-4fc6-bde1-f1333d29b54d
doi:10.2478/johh-2014-0010

Subject:

river flow forecasting, forecast lead time, error updating, artificial neural network, and genetic algorithm

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

Slovak

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

60-74

Source:

Journal of hydrology and hydromechanics | 2014 Volume:62 | Number:1

Harvested from:

CDK

Metadata only:

false