Intelligent Predictive Models for Water resources Engineering

Fresh water is considered to be one of the most important resources for humans and the environment. Due to the increase in population and the currently unsustainable usage of this limited resource, more attention is needed in the management of water resources. Advanced computational methods can help in attaining a better understanding of all aspects of water. Indeed, a better understanding of water resources requires a vast knowledge of a wide variety of fields such as atmospheric science, geology, hydrology, hydraulics and mathematics etc. To assist in this process computing techniques have been widely applied in water resources engineering problems. An artificial neural network (ANN) has been applied to solve many engineering problems since the 1980s. However, there are still many engineering fields that have the potential to benefit from ANN, such as water resource engineering. In the present research two important applications; time-series prediction and function estimation for water resource engineering are investigated. Within water engineering the prediction of river discharge is important. The results can be used for many purposes including flooding management, risk assessment and saving lives. New techniques are always being sought to improve the accuracy of predictions. In the first part of this research a neural network model was developed as a tool for time-series prediction to forecast water flow discharge of Fitzroy River near Rockhampton in central Queensland. A feed-forward back-propagation network was selected to predict the daily time-series of the Fitzroy Rivers’ discharge at The Gap station, Queensland. The data was derived from the Queensland Department of Natural Resources and Mines. The two developed ANN models are investigated and compared after many trials with a number of inputs, outputs, hidden layers, learning rate and transfer functions. The final model uses the flow data for 15 successive days and then predicts the discharge for the next 4 days. The results show that an accurate ...