Application of the Kohonen neural network in coastal water management: methodological development for the assessment and prediction of water quality.

Kohonen neural network (KNN) was applied to nutrient data (ammonia, nitrite, nitrate and phosphate) taken from coastal waters in a Spanish tourist area. The activation maps obtained were not sufficient to evaluate and predict the trophic status of coastal waters. To achieve this aim, a new methodology is proposed which uses as its starting point the activation maps obtained from KNN. Firstly, to evaluate the trophic status of the coastal waters, it consists of the development of a quadrat system which enables a better classification than the traditional classification based simply on standardized data. The new classification allows clear differentiation of water quality within the mesotrophic band. Secondly, and in order to use the activation maps as predictive tools, the trophic classification, obtained from activation maps, was transposed onto new activation maps. To do this, the activation maps of the sampling points which defined each trophic group were superimposed. To avoid unnecessary complexity and to facilitate the process, this superimposition was undertaken only where the frequency exceeded 0.05. In this way, four frequency maps related to the trophic status of coastal waters (potentially eutrophic, high mesotrophic, low mesotrophic and oligotrophic) were obtained. There was no loss of relevant information in the new maps thus obtained. These frequency maps served as the basis for the successful prediction of the trophic status of random samples of coastal waters. This methodology, based on KNN, is proposed as a tool to aid the decision-making in coastal water quality management.