Prediction of dissolved oxygen (DO) and total dissolved solids (TDS) are of paramount importance for water environmental protection and analysis of the ecosystem. The traditional methods for water quality prediction are suffering from unadjusted hyper-parameters. To effectively solve the hyper-parameter setting problem, the present study proposes a framework for tuning the hyper-parameters of feed forward neural network (FFNN) and gene expression programming (GEP) with particle swarm optimization (PSO). Thereafter, the PSO coupled hybrid feed forward neural network (PSO-FFNN) and hybrid gene expression programming (PSO-GEP) were used to predict DO and TDS levels in the upper Indus River. Based on thirty years consistent dataset, the most influential input parameters for DO and TDS prediction were determined using principal component analysis (PCA). The impact on the model performance was evaluated employing five statistical evaluation techniques. Modeling results indicated excellent searching efficiency of the PSO algorithm in optimizing the structure and hyper-parameters of the FFNN and GEP. Results of PCA revealed that magnesium, chloride, sulphate, bicarbonates, specific conductivity, and water temperature are appropriate inputs for DO modeling, whereas; calcium, magnesium, sodium, chloride, bicarbonates and specific conductivity remained the influential parameters for TDS. Both the proposed hybrid models showed better accuracy in predicting DO and TDS, however, the hybrid PSO-GEP model achieves better accuracy than the PSO-FFNN with R value above 0.85, the root mean squared error (RMSE) below 3 mg/l and performance index value close to 1. The external validation criteria confirmed the resolved overfitting issue and generalized results of the models. Cross-validation of the model output attained the best statistical metrics i.e. (R = 0.87, RMSE = 2.67) and (R = 0.895, RMSE = 2.21) for PSO-FFNN and PSO-GEP model, respectively. The research findings demonstrated that the implementation of artificial intelligence models with optimization routine can lead to optimized models for accurate prediction of water quality.

溶解氧 (DO) 和总溶解固体 (TDS) 的预测对于水环境保护和生态系统分析至关重要。传统的水质预测方法受到未调整的超参数的影响。为了有效解决超参数设置问题，本研究提出了一个框架，用于通过粒子群优化 (PSO) 调整前馈神经网络 (FFNN) 和基因表达编程 (GEP) 的超参数。此后，PSO 耦合混合前馈神经网络 (PSO-FFNN) 和混合基因表达编程 (PSO-GEP) 被用于预测印度河上游的 DO 和 TDS 水平。基于三十年一致的数据集，使用主成分分析 (PCA) 确定了对 DO 和 TDS 预测最有影响的输入参数。使用五种统计评估技术评估对模型性能的影响。建模结果表明 PSO 算法在优化 FFNN 和 GEP 的结构和超参数方面具有出色的搜索效率。PCA 的结果表明，镁、氯化物、硫酸盐、碳酸氢盐、比电导率和水温是 DO 建模的合适输入，而；钙、镁、钠、氯化物、碳酸氢盐和比电导率仍然是 TDS 的影响参数。所提出的两种混合模型在预测 DO 和 TDS 方面都表现出更好的准确性，但是，混合 PSO-GEP 模型比 R 值高于 0.85、均方根误差 (RMSE) 低于 3 mg/l 的 PSO-FFNN 实现了更好的准确性和性能指标值接近1。外部验证标准确认了已解决的过拟合问题和模型的泛化结果。模型输出的交叉验证分别获得了 PSO-FFNN 和 PSO-GEP 模型的最佳统计指标，即（R = 0.87，RMSE = 2.67）和（R = 0.895，RMSE = 2.21）。研究结果表明，实施具有优化程序的人工智能模型可以产生用于准确预测水质的优化模型。