An expert system based on fish assemblages for evaluating the ecological quality of streams and rivers
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- 作者：Michele Scardi ; Stefano Cataudella ; Paola Di Dato ; Eugenio Fresi ; Lorenzo Tancioni
- 关键词：Expert systems ; Neural networks ; Fish assemblages ; Biotic indices ; Ecological status ; Lotic ecosystems
- 刊名：Ecological Informatics
- 出版年：2008
- 出版时间：1 January 2008
- 年：2008
- 卷：3
- 期：1
- 页码：55-63
- 全文大小：608 K
文摘
Fish species have been often used as indicators of environmental quality in aquatic ecosystems, while biotic indices based on fish have become common tools in ecological monitoring. Nevertheless, such indices are far from perfect, mainly because they are based on assumptions that sometimes are not met and because they cannot be optimized from a computational point of view. As any other method, they rely upon expert judgments for selecting relevant metrics, combining metrics into a score and defining thresholds between ecological status classes in the scoring scale. Provided that no procedure can be entirely objective in evaluating ecological status, as this very concept is inherently subjective, we propose a novel approach in which the unavoidable subjective elements only play a role in the earliest steps, while the subsequent optimization of the evaluation procedure is as objective as possible. An expert system, designed after this concept for Latium (Central Italy) river basins and based on a multilayer perceptron neural network, was developed and implemented into a Graphical User Interface (GUI) in order to make it easily accessible to non-technical users. The neural network reconstructs experts' judgments on the basis of a set of abiotic descriptors and fish assemblage composition, thus providing consensus estimates of ecological status for any river stretch. This approach allows easily the incorporation into the expert system of new data and new expert judgments as soon as they become available. However, the very first version of the expert system is already able to correctly classify 2 out of 3 cases, while the worst classification error does not exceed a single class of ecological status.