RT Journal Article
T1 Enhancing water quality prediction for fluctuating missing data scenarios: A dynamic Bayesian network-based processing system to monitor cyanobacteria proliferation
A1 Pazo Rodriguez, María
A1 Gerassis Davite, Saki
A1 Araújo Fernández, María
A1 Margarida Antunes, I.
A1 Rigueira Diaz, Xurxo
K1 3212 Salud Publica
K1 3108.01 Bacterias
AB Tackling the impact of missing data in water management is crucial to ensure the reliability of scientific research that informs decision-making processes in public health. The goal of this study is to ascertain the root causes associated with cyanobacteria proliferation under major missing data scenarios. For this purpose, a dynamic missing data management methodology is proposed using Bayesian Machine Learning for accurate surface water quality prediction of a river from Limia basin (Spain). The methodology used entails a sequence of analytical steps, starting with data pre-processing, followed by the selection of a reliable dynamic Bayesian missing value prediction system, leading finally to a supervised analysis of the behavioral patterns exhibited by cyanobacteria. For that, a total of 2,118,844 data points were used, with 205,316 (9.69 %) missing values identified. The machine learning testing showed the iterative structural expectation maximization (SEM) as the best performing algorithm, above the dynamic imputation (DI) and entropy-based dynamic imputation methods (EBDI), enhancing in some cases the accuracy of imputations by approximately 50 % in R2, RMSE, NRMSE, and logarithmic loss values. These findings can impact how data on water quality is being processed and studied, thus, opening the door for more reliable water management strategies that better inform public health decisions
PB The Science of The Total Environment
SN 00489697
YR 2024
FD 2024-06-01
LK http://hdl.handle.net/11093/7042
UL http://hdl.handle.net/11093/7042
LA eng
NO The Science of The Total Environment, 927, 172340 (2024)
NO Agencia Estatal de Investigación | Ref. PID2020-116013RB-I00
DS Investigo
RD 21-abr-2025