Water Quality Prediction in Freshwater Reservoirs under Climatic Variability Using Bayesian Networks

Water quality sampling in freshwater reservoirs is economically
and logistically demanding, limiting monitoring frequency. This study
evaluates water quality dynamics and identifies key controlling variables
in two Mexican reservoirs: Necaxa and Manuel Ávila Camacho
(Valsequillo). Using multivariate statistical techniques and Bayesian
Networks, a probabilistic machine learning framework was implemented
to quantify variable influence and explore scenario-based responses.
Results reveal significant spatiotemporal variability driven by local
conditions and seasonal factors primarily precipitation and temperature,
which exacerbate pollution levels during warm and rainy months. Critical
parameters identified include Total Suspended Solids, Total Organic
Carbon, Chemical Oxygen Demand, and Escherichia coli, which compromise agricultural use and aquatic ecosystem sustainability.
Based on these findings, an optimized scheme of eight key variables
is proposed for each reservoir according to its specific use. This
research demonstrates that combining Bayesian Networks with multivariate
analysis provides a robust decision-support tool for contexts with
limited data, promoting efficient, adaptive, and sustainable water
resource management strategies.