Journal Article

A novel interpretable hybrid model for multi-step ahead dissolved oxygen forecasting in the Mississippi River basin

Moradi, Mansour, Moghaddasi, Mahnoosh, Mohammadi Ghaleni, Mehdi, Ali, Hayder Mohammed

2024

Overview

Accurate forecasting of dissolved oxygen (DO) levels is vital for river ecosystem health. A novel methodology, MVMD-TSA-GPR, combines Multivariate Variational Mode Decomposition (MVMD), the Tunicate Swarm Algorithm (TSA), and Gaussian Process Regression (GPR) to improve DO level predictions. This study also incorporated Generalized Additive Model and Regression Bagged Ensemble (RBE) for 1- and 3-month forecasts using monthly data (1974–2023) from 16 water quality parameters across five Mississippi River basin sites. Key predictors identified through cross-correlation include lagged values and parameters like water temperature, discharge, pH, total phosphorus, potassium, and sulfate, which significantly influence DO levels. The MVMD-TSA-GPR model outperformed others, especially at site 5, showing substantial improvements in accuracy with decreased RMSE values across various scenarios. Model ranking via the Taylor Diagram indicated MVMD-TSA-GPR had the highest performance, followed by MVMD-TSA-RBE and others. Notably, the GPR model’s RMSE at site 3 decreased from 2.11 to 1.01 (109% reduction) for the 1-month forecast, while at site 4 for the 3-month forecast, it dropped from 1.85 to 1.04 (106% reduction). The results revealed that the MVMD-TSA-GPR model demonstrated the highest performance for DO (t + 1), achieving R = 0.90, PBIAS = 0.73%, and WI = 0.804, as well as for DO (t + 3), with R = 0.88, PBIAS = 0.54%, and WI = 0.779, at the Lower Mississippi site during the test phase. Additionally, the MVMD-TSA-RBE model excelled for DO (t + 1) in the Missouri River at the Hermann site, achieving R = 0.91, PBIAS = 0.86%, and WI = 0.805 during the test phase. These results underscore the effectiveness of the MVMD-TSA hybrid approach. Interpretative analysis using SHapley Additive exPlanations (SHAP) revealed water temperature, pH, and potassium as key factors affecting DO levels. The speed and accuracy of MVMD-TSA-GPR make it a promising tool for monitoring river water quality.

• Publisher: Springer Nature B.V
• Subject: Accuracy / Algorithms / Aquatic ecosystems / Cross correlation / Dissolved oxygen / Environmental monitoring / Forecasting / Gaussian process / Parameter identification / Potassium / Regression models / Relative biological effectiveness (RBE) / River basins / Rivers / Water discharge / Water quality / Water temperature