Optimization of water quality monitoring programs by data mining

Abstract

Water quality monitoring programs are essential planning and management tools, but they face many challenges in the developing world. The scarcity of financial and human resources and the unavailability of infrastructure often make it impossible to meet the legal requirements of water monitoring. Many approaches to optimizing water quality monitoring programs have already been proposed. However, few investigations have developed and tested data mining for this purpose. This article has developed data-based models to reduce the number of water quality parameters of monitoring programs using data mining. The objective was to extract patterns from the database, expressed by association rules, which together with field parameters, measured with automatic probes, can estimate laboratory variables. This approach was applied in 35 monitoring stations along 27 river basins throughout Brazil. The data are from fifty years of monitoring (1971–2021), constituting 6328 observations of 60 water quality parameters investigated in different environmental contexts, water quality, and the structuring of monitoring programs. With the applied approach it was possible to estimate 56% of the laboratory parameters in the monitoring stations investigated. The influence of environmental characteristics on the optimization capacity of monitoring programs was evident. The methodology used was not influenced by different water quality levels and anthropogenic impacts. However, the number of parameters was the most influential element in optimization. Monitoring programs with 20 or more water quality variables have the highest potential (≥44%) of optimization by this methodology. Results demonstrate that this approach is a promising alternative that can reduce the frequency of analyses measured in the laboratory and increase the spatial and temporal coverage of water quality monitoring networks.