A multi-model data fusion methodology for reservoir water quality based on machine learning algorithms and bayesian maximum entropy

A major concern in the management of reservoirs is water quality because of the negative consequences it has on both environment and human life. Artificial Intelligence (AI) concept produces a reliable framework to recognize complicated and non-linear correlations between input and output data. Although various machine learning (ML) algorithms in recent studies were employed to predict water quality variables, the existing literature lacks exploring the combination of these algorithms, which has the potential to significantly amplify the outcomes achieved by individual models. Thus, the current study aims to bridge this knowledge gap by evaluating the precision of Random Forest Regression (RFR), Support Vector Regression, Multilayer Perceptron (MLP), and Bayesian Maximum Entropy-based Fusion (BMEF) models to estimate such water quality variables as dissolved oxygen (DO) and chlorophyll-a (Chl-a). The comparisons were conducted in two primary stages: (1) a comparison of the outcomes of different ML algorithms with each other, and (2) comparing the ML algorithms' findings with that of the BMEF model, which considers uncertainty. These comparisons were evaluated using robust statistical measures, and, finally, to indicate the utility and efficacy of the newly introduced framework, it was efficiently utilized in Wadi Dayqah Dam, which is situated in Oman. The findings indicated that, throughout both training and testing phases, the BMEF model outperformed individual machine learning models, namely MLP, RFR, and SVR by 5%, 26%, and 10%, respectively, when R2 and Chl-a are considered as evaluation index and water quality variables, respectively. Additionally, as the individual ML models are not capable of predicting electrical conductivity and oxidation-reduction potential efficiently, the BMEF model leads to better results by R2=0.89, which outperforms MLP (R2=0.81), RFR (R2=0.79), and SVR (R2=0.62) for oxidation-reduction potential. Regarding the study limits of the present study, spatio-temporal data should be collected over a long time to increase the data frequency and reduce the uncertainty related to climate variability.