An Operational Riverflow Prediction System in Helmand River, Afghanistan Using Artificial Neural Networks

Abstract

This study uses historical flow record to establish an operational riverflow prediction model in Helmand River using artificial neural networks (ANNs). The tool developed for this research demonstrates that the ANN model produces results with a very short turn-around time and with good accuracy. This river system used for this demonstration is quite complex and contains uncertainties associated with the historical record. These uncertainties include downstream flow rates that are not always higher than the combined upper stream values and only one continuously operating stream gage in the headwaters. With these characteristics, improvements in the hydrologic predictions are achieved by using a best additional gage search and a two-layered ANN strategy. Despite the gains demonstrated in this research, better simulation accuracy can be achieved by constructing a new knowledge base using more recent information on the hydrologic/hydraulic condition changes that have occurred since the available period for 1979. Follow-on research can also include developing extrapolation procedures for desired project events outside the range of the historical data and predictive error correction analysis.