Fuzzy modeling of interactions among environmental stressors in the ecosystem of Lake Koronia, Greece.

Abstract

The development of a model for assessing the impact and interactions of stressors in the ecosystem of Lake Koronia, based on fuzzy inference, is presented in this paper. The proposed fuzzy inference model assesses the synergistic interactions among several significant stressors on fish production. These stressors include industrial pollution, pesticide and nutrient usage due to agricultural activities, and water level decrease due to irrigation works. Apart from the experts' knowledge, expressed in a set of fuzzy rules, a number of parameters such as pH, conductivity, biochemical and chemical oxygen demand, and nitrate concentration were used as stressor indicators. The proposed model is capable of simulating the effect of a large variety of environmental conditions, and it can be used as a dynamic tool for ecosystem risk assessment since it produces both qualitative and quantitative results, allowing for comparisons of predictions with on-going observational research and ecosystem monitoring. Its operation was successfully verified for a number of different conditions, ranging from low stressor impact to high stressor impact (where, in fact, the fish production was diminished). Moreover, the proposed fuzzy inference model can be used as a tool for the investigation of the behavior of the aquatic ecosystem under a large number of hypothetical environmental risk scenarios.