Investigation of artificial neural network models for streamflow forecasting

Time series forecasting is the use of a model to forecast future events based on known past events. Accurate forecasts of time series variables at different time scales are becoming increasingly necessary to facilitate mitigation of negative impacts of climate change, maximisation of system benefits from improved planning and management, and minimisation of system failure risks in all social, economical and environmental activities. Examples include hourly forecasts of rainfall (1-48 hours ahead), which are useful for flood warning, and monthly forecasts of streamflow (1-12 months ahead), which are beneficial for planning and operation of water supply systems. There are many other applications in finance and economics such as tourism demand forecasting and stock forecasting.