## 1 Introduction
Some heuristic approaches (e.g., MLP, FFNN, ANFIS, and RBFNN) have been applied to ponds, lakes, and reservoirs [5; 33; 34; 35; 36; 37]. [PERSON] et al. [4] introduced two ANNs for estimating water quality parameters (i.e., DO and BOD) in the Gomi River, India. [PERSON] [38] proposed an autoregressive integrated moving average (ARIMA)-based neural network (ARIMA-NN) to predict water quality parameters (e.g., temperature, boron, and DO) in the Buyuk Menderes River, Turkey. [PERSON] and [PERSON] [10] proposed the MLP, RBFNN, and multilinear regression (MLR) to estimate the DO concentration in Fountain Creek, Colorado. [PERSON] et al. [39] developed a self-organizing RBF (SORBF) to predict the DO concentration in activated sludge wastewater treatment processes. [PERSON] et al. [40] proposed the MLP, gene expression programming (GEP), and MLR to estimate the DO concentration at a sand filter outlet. [PERSON] et al. [41] used the generalized regression neural network (GRNN) for forecasting the DO concentration in the Danube River, Serbia. [PERSON] [42] proposed the grid partition-based ANFIS (ANFIS-GRID), subtractive clustering based ANFIS (ANFIS-SUB), and MLR for estimating the DO concentration in the Klamath River, Oregon. [PERSON] et al. [43] applied the ANFIS for the prediction of DO concentration. [PERSON] et al. [44] investigated ANFIS, MLP, and MLR to estimate the DO concentration in the Tai Po River, Hong Kong. [PERSON] and [PERSON] [45] developed the modified response surface method (MRSM) and MLP to estimate the DO concentration. Although there have been many studies to estimate the DO concentration using heuristic approaches, the applications of CCNN have been limited. [PERSON] et al. [8] used the MLP, RBFNN, SVM, and linear genetic programming (LGP) for predicting the DO concentration in the Delaware River, New Jersey. [PERSON] et al. [46] determined the extrapolation of an ANN model, which was established for predicting the DO concentration in the Danube River. In addition, a specific investigation using heuristic and decomposition approaches, for example, the hybrid approaches using DWT [47; 48; 49; 50] and VMD [51], cannot be found with ease for DO forecasting/prediction category [12; 52]. [PERSON] et al. [48] proposed a hybrid model based on wavelet analysis (WA), least squares support vector regression (LSSVR), and an optimal improved Cauchy particle swarm optimization (CPSO) algorithm for predicting the DO concentration. The WA-LSSVR-CPSO model demonstrated a powerful and reliable approach for predicting the DO concentration in intensive aquaculture. [PERSON] et al. [50] evaluated 30-min DO concentration using the ANN and wavelet-based ANN (WANN) models in the River Calder, England. Results showed that the WANN model provided a 30-min DO concentration prediction that was comparable to ANN. [PERSON] et al. [51] discussed two hybrid models based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN), VMD, extreme learning machines (ELMs), and least squares support vector machines (LSSVMs) models for predicting chlorophyll-a (Chl-a) and DO concentrations, respectively. They found that the CEEMDAN-VMD-ELM model produced the best results to predict water quality parameters (e.g., Chl-a and DO concentration). This paper proposes two heuristic models (e.g., MLP and CCNN) and decomposition approaches (e.g., DWT and VMD) for predicting the DO concentration in the St. Johns River, Florida, USA. The model performances are evaluated using model efficiency indexes and diagnostic analysis using graphical comparisons (e.g., Taylor diagram and violin plot). The paper is organized as follows: The second part provides methodologies, including MLP, CCNN, DWT, and VMD, respectively. The third part proposes a study area and data, and the fourth part presents results and discussion. Conclusions are found at the end of the paper.