Modeling the dynamic of stratified dissolved oxygen using a data-driven regression approach

Monitoring dissolved oxygen (DO) levels is crucial for effective water management and conservation strategies. However, monitoring the water quality for stratified DO using in situ sensors requires substantial time and operational costs. Data-driven approaches, particularly machine learning (ML) models, offer a promising and cost-efficient alternative for predicting DO dynamics in aquatic systems. This research develops data-driven prediction models for stratified DO in Lake Maninjau using single-target regression (STR) and multiple-target regression (MTR) approaches. The results demonstrate that the STR approach, which leverages a long-term temporal dataset, yields superior predictive performance compared with the MTR approach that relies on instantaneous, concurrently recorded observations. In the STR framework, near-surface DO is predicted from multilayer water temperature profiles, whereas in the MTR framework, stratified DO at multiple depths is estimated from a set of multilayer water quality variables. Both approaches evaluate five regression models: multilinear, polynomial, support vector, random forest, and extreme gradient-boosting regression. To address the high dimensionality of the predictors, recursive feature reduction is applied to each model. The result indicates that the vertical DO structure can be effectively represented by an upper layer (0 m and 2 m depths) and a lower layer (21 m depth). However, the MTR approach exhibits reliable performance for one prediction target while failing to generalize adequately across all target depths. Validation indicates that tree-based models, random forest and extreme gradient boosting, predict near-surface DO well in STR-MTR. In MTR, multilinear regression best predicts DO at 2 m, while support vector regression (SVR) best predicts surface DO. All models identify water temperature at 2 m as the main driver in both approaches, with chlorophyll fluorescence and salinity also important near the surface in MTR. Future research should focus on the spatial-temporal interactions between these factors to improve our understanding of dissolved oxygen dynamics, which is critical for the health of aquatic ecosystems.