# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach  
[PERSON]  
1 Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong University, Chongqing 400074, China 12 Schongqing Xike Water Transport Engineering Consulting Co., Ltd., Chongqing Design Group Co., Ltd., Chongqing 400074, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Schongqing Xike Water Transport Engineering Consulting Co., Ltd., Chongqing Design Group Co., Ltd., Chongqing 400074, China; [EMAIL_ADDRESS]  
[PERSON]  
2 Schongqing Xike Water Transport Engineering Consulting Co., Ltd., Chongqing 400074, China 2 Schongqing Xike Water Transport Engineering Consulting Co., Ltd.  
DO modeling is often achieved by process-based and data-driven approaches. Process-driven models rely on the mathematical representation of the physical processes [3]. Examples include the River and Stream Water Quality Model, the Environmental Fluid Dynamics Code, and the Water Quality Analysis Simulation Program [4; 5; 6]. These mechanistic models are based on well-established processes and require substantial amounts of data for calibration. However, the data demands are influenced by their structural complexity. A lack of specific datasets, such as those related to topography, flow, and water quality, can compromise model performance [7]. Data-based approaches are an alternative to DO forecasting. The models make predictions based on patterns and insights learned from data, rather than relying on theoretical or physical principles [8]. Machine learning (ML) and artificial intelligence (AI) are increasingly used for DO modeling. Artificial neural networks (ANNs) are a classic ML method for predicting DO. The models are composed of layers of interconnected nodes (neurons) that work together to recognize patterns and make decisions. Examples include DO estimation in rivers [9; 10], reservoirs [11], urban lakes [12], and wastewater [13]. Similar ML techniques are also employed in previous studies. For example, [PERSON] et al. [13] developed a feed-forward neural network (FFNN) model to predict the DO from biochemical and chemical oxygen demand, producing satisfactory results. [PERSON] et al. [14] proposed a hybrid support vector regression (SVR)-based model to estimate DO in river crab culture ponds, making it a reliable forecasting tool in modern intensive aquaculture. [PERSON] et al. [15] conducted a comparative study of ANN, SVM, and genetic programming (GP) for DO prediction in the Delaware River, USA, illustrating that SVM outperformed the other models. [PERSON] and [PERSON] [16] constructed an extreme learning machine to model DO levels in the backwater zone of the Three Gorges Reservoir. It was concluded that the model can be applied to low-impacted rivers, but it may lose accuracy in polluted rivers, as it is challenging to consider anthropogenic influences. Adaptive Neurofuzzy inference systems [17], K-means clustering [18], decision trees [19], etc., are reported, demonstrating ML's potential for DO modeling. Advanced AI techniques, particularly deep learning (DL), are gaining growing interest. One popular option is the long short-term memory (LSTM) model. For instance, [PERSON] et al. [20] created an LSTM to study river DO dynamics from sparse measurements and intensive hydrometeorology data. The model effectively captured DO solubility and the decreasing trend with rising water temperatures, highlighting its potential for DO modeling in chemically ungauged basins. [PERSON] et al. [21] compared LSTM with GP, group method of data handling (GMDH) network, SVR, and Gaussian process regression. It was concluded that the most advanced model does not necessarily produce the most accurate predictions (the GMDH was the best). [PERSON] et al. [22] implemented and evaluated two process-guided deep learning (PGDL) approaches for predicting daily DO concentrations in rivers from the Delaware River Basin, USA.