الفهرس 
HYDROLOGY OF THE NILE BASINOnly 14 pages are availabe for public view
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Abstract
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Abstract
Forecasting of the annual quantity of available water has always been a problem, not only
for the Nile River, but for all rivers of the world. This forecasting is crucially essential for project
planning, dam operating rules, and optimum utilization of the river water. Inflow forecasting is
a key input to the process of reservoir management. Accurate forecasts increase the benefits such
as more energy generation, more effective drought mitigation. and better flood protection.
owadays, the Artificial Neural Networks (ANND have received a great deal of attention
and are being touted as one of the computational tools ever developed. It has been applied widely
in different fields. Because of its ability to imitate human-like brain functions, ANNT can
potentially constitute powerful tool for hydrological forecasting.
Therefore, this thesis is directed to utilize the available seasonal univariate neural network
forecasting model in the multivariate dimension. In other words, the available seasonal ANNT
forecasting model is based only on the previous natural inflows at Aswan. However, in the
multivariate case, the natural flow at Aswan is related to the natural flow at the main upstream
stations, Malakal, Khartoum and Atbara as well as at Aswan itself. The flows at the upstream
stations along with the previous flows at Aswan have been used to train the multivariate model
to forecast the current natural flow at Aswan. The contribution of the past inflows of the three
upstream stations to forecast the inflows at Aswan has been considered in three different
manners. The idea is, for every upstream station, how many months in past should be
incorporated to predict the current inflow at Aswan. Both univariate and multivariate neural
network models have been trained over the same historical period. For both models, validation
using the data of recent years, has been done.
The thesis contains description of the major characteristics of the Nile River Basin,
specially its main watersheds, tributaries and discharges at the different gaging stations. It also
reviews the previous studies related to the applications of the ANNT in hydrology. The thesis
briefs the concepts of Artificial Neural Networks. Different architectures, transfer functions,
single and multiple layers of neurons, and training procedures are also high lighted in a brief way.
Finally, the performance of the neural network ability in forecasting drought, flood, and average year against some other techniques namely; Corridor and Regression, have been tested
. and presented.
As a global conclusion and results of the study, the seasonal neural network models have
many parameters but can potentially generate the most accurate forecasts. However, their
performance depends on the stage of training and, perhaps, on the size of the training data set.
The discrepancies found between the desired and simulated output by the univariate and
multivariate ANNT models should not be attributed only to the multivariate approach; the data
sets used should take some (if not all) of the blame.
The thesis recommends to carry out further research to investigate and compare results
using different neural network models structures. Another model structures with different
learning rates, hidden nodes, and number of layers are needed to be studied.