High Accuracy Dissolved Oxygen Prediction in Water Quality Analysis with A‐XG‐Q Hybrid Model

This study presents A‐XG‐Q, a groundbreaking hybrid model for predicting dissolved oxygen (DO) levels in water quality analysis that integrates ARIMA, XGBoost, and QAOA. ARIMA captures linear trends and seasonal patterns, XGBoost models complex nonlinear relationships, and QAOA optimizes hyperparameters such as learning rate and tree depth for computational efficiency. Using a Kaggle dataset spanning 1989–2019, the model achieved an R 2 of 0.990 and an RMSE of 0.050 despite missing data (36% DO, 96% air temperature), demonstrating exceptional accuracy. Temporal analyses revealed seasonal variations in DO and temperature, while Secchi depth and water depth remained stable. Correlation analysis identified a negative DO‐water temperature relationship, providing ecological insights. QAOA optimization reduced training time, enabling real‐time monitoring applications. By combining classical statistical methods, advanced machine learning, and quantum optimization, A‐XG‐Q outperforms many hybrid models and effectively handles data variability and missing values. This work advances environmental data science by providing a robust framework for sustainable water resource management and informed policy making, with potential for broader applications in ecosystem monitoring and environmental forecasting. The model's high performance underscores its value in addressing complex environmental challenges and supporting sustainable development goals.