A novel method for training neural networks for time-series prediction in environmental systems

• Published in: Ecological modelling Vol. 162; no. 1; pp. 87 - 95
• Main Authors: Aitkenhead, M.J., McDonald, A.J.S., Dawson, J.J., Couper, G., Smart, R.P., Billett, M., Hope, D., Palmer, S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-04-2003; Elsevier
• Subjects: Animal, plant and microbial ecology; Backpropagation; Biological and medical sciences; Environmental prediction; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Methods and techniques (sampling, tagging, trapping, modelling...); Neural networks; Simulated annealing; Stream dynamics; Time-series prediction; United Kingdom; Scotland; Great Britain; Europe; Time-series prediction; Backpropagation; Environmental prediction; Neural networks; Stream dynamics; Simulated annealing; Climate; Fresh water; Time series; Neural network; Multivariate analysis; Forecast model; Modeling; Stream dynamics: Time-series predictions Neural networks; Statistical method; Soils; Watershed; Environment
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/S0304-3800(02)00401-5

Soil, streamwater and climatic variables were measured hourly over several month periods in two situations in North-East (NE) Scotland, using data loggers and other measuring instruments. One of the locations was on agricultural land near Inverness while the other was at an acidic peat moorland site in the River Dee catchment. The data sets were used to train neural networks using three different methods, including a novel, biologically plausible system. Temporal pattern recognition capabilities using each method were investigated. The novel method proved equally capable in predicting future variable values using large data sets as the other two methods. An argument is made for this method, termed the ‘Local Interaction’ method, providing valid competition to other neural network and statistical methods in the detection of patterns and prediction of events in complex biological systems.