Combining Artificial Neural Network and Driver–Pressure–State–Impact–Response Approach for Evaluating a Mediterranean Lake

Abstract

The main objective of this research was to evaluate the surface water system of Lake Vegoritida (Region of Central Macedonia, Greece). The Driver–Pressure–State–Impact–Response (DPSIR) methodological approach was used. The analysis includes data from three (3) stations monitoring point source pollution and recording the most critical water quality measurement parameters in a time series data analysis from 1983 to 1997. The data will contribute to the analysis and was used to investigate, identify, and evaluate possible sources of chemical and ecological changes recorded in the lake. The artificial neural network (ANN) is a valuable tool for making predictions based on the water quality data set. The findings highlighted the increased concentration of nutrients that contribute to the presence of eutrophic conditions, while their seasonal variability is mainly due to factors, such as water level fluctuations and biological processes in the lake. The above, combined with the critical biotic indicators and factors alongside the reduction in biodiversity, indicated that only the most resistant species survive, confirming the previous finding. In Greece, systematic monitoring and reporting programs have recently been implemented, such as the ECOFRAME scheme and the guidelines proposed by the “Intercalibration Group for Mediterranean Lakes”. The water quality status could be classified as “High”, “High to Good”, and “High to Poor”, respectively, while the overall ecological assessment tends to change to poor conditions. The actions required at an early stage concern the planning of programs and actions that contribute to the sustainable management of land uses and the reduction in point sources of pollution, as well as the reduction of the applied quantities of agrochemicals on the cultivated land in the study area.