| Title: | River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin |
| Authors: | Akhtar, M.K., G.A. Corzo, S.J. van Andel, and A. Jonoski |
| Year: | 2009 |
| Journal: | Hydrology and Earth System Sciences |
| Volume (Issue): | 13(9) |
| Pages: | 1607-1618 |
| Article ID: | |
| DOI: | 10.5194/hess-13-1607-2009 |
| URL (non-DOI journals): | |
| Model: | SWAT |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | model comparison |
| Secondary Application Category: | hydrologic assessment |
| Watershed Description: | 907,000 km^2 Ganges River, which drains all of Nepal, much of northern India and part of southwest China. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | SWAT was used in a minor way in this study. The main results were generated with the ANN model. |
| Abstract: | This paper explores the use of flow length and
travel time as a pre-processing step for incorporating spatial
precipitation information into Artificial Neural Network
(ANN) models used for river flow forecasting. Spatially distributed
precipitation is commonly required when modelling
large basins, and it is usually incorporated in distributed
physically-based hydrological modelling approaches. However,
these modelling approaches are recognised to be quite
complex and expensive, especially due to the data collection
of multiple inputs and parameters, which vary in space
and time. On the other hand, ANN models for flow forecasting
are frequently developed only with precipitation and
discharge as inputs, usually without taking into consideration
the spatial variability of precipitation. Full inclusion of
spatially distributed inputs into ANN models still leads to a
complex computational process that may not give acceptable
results. Therefore, here we present an analysis of the flow
length and travel time as a basis for pre-processing remotely
sensed (satellite) rainfall data. This pre-processed rainfall is
used together with local stream flow measurements of previous
days as input to ANN models. The case study for this
modelling approach is the Ganges river basin. A comparative
analysis of multiple ANN models with different hydrological
pre-processing is presented. The ANN showed its
ability to forecast discharges 3-days ahead with an acceptable
accuracy. Within this forecast horizon, the influence of
the pre-processed rainfall is marginal, because of dominant
influence of strongly auto-correlated discharge inputs. For
forecast horizons of 7 to 10 days, the influence of the preprocessed
rainfall is noticeable, although the overall model
performance deteriorates. The incorporation of remote sensing
data of spatially distributed precipitation information as
pre-processing step showed to be a promising alternative for
the setting-up of ANN models for river flow forecasting |
| Language: | English |
| Keywords: | |