Neural network time series prediction of environmental variables in a small upland headwater in NE Scotland

Short-term prediction of environmental variables such as stream flow rate is useful to members of the general public and environmental scientists alike, providing the ability to predict environmental disasters or scientifically interesting events. Here, a neural-network based method is presented, which is capable of providing advance flood warnings or the prediction of high stream flow events for research purposes in a small upland headwater in NE Scotland. This method relies on training from past time series data acquired in the field, and provides the ability to predict a range of hydrological and meteorological variables up to 24 It ahead using feedback of predicted values at time t as new inputs for the next time step t + 1. The system is rapid and effective, relies on standard neural network training methods, and has the potential to be implemented in a web-based monitoring and prediction package. The model design could be implemented at any study site where time series data has been gathered, and is sufficiently flexible to accept whatever data is available. Copyright (C) 2007 John Wiley & Sons, Ltd.