Forecasting cyanobacterial concentrations using B-spline networks

Abstract

Artificial neural networks have been used successfully in a number of areas of civil engineering, including hydrology and water resources engineering. In the vast majority of cases, multilayer perceptrons that are trained with the back-propagation algorithm are used. One of the major shortcomings of this approach is that it is difficult to elicit the knowledge about the input/output mapping that is stored in the trained networks. One way to overcome this problem is to use B-spline associative memory networks (AMNs), because their connection weights may be interpreted as a set of fuzzy membership functions and hence the relationship between the model inputs and outputs may be written as a set of fuzzy rules. In this paper, multilayer perceptrons and AMN models are compared, and their main advantages and disadvantages are discussed. The performance of both model types is compared in terms of prediction accuracy and model transparency for a particular water quality case study, the forecasting (4 weeks in advance) of concentrations of the cyanobacterium Anabaena spp. in the River Murray at Morgan, South Australia. The forecasts obtained using both model types are good. Neither model clearly outperforms the other, although the forecasts obtained when the B-spline AMN model is used may be considered slightly better overall. In addition, the B-spline AMN model provides more explicit information about the relationship between the model inputs and outputs. The fuzzy rules extracted from the B-spline AMN model indicate that incidences of Anabaena spp. are likely to occur after the passing of a flood hydrograph and when water temperatures are high.