Water turbidity is a critical index of water quality due to its high correlation with the five main water quality parameters (electrical conductivity, nitrogen, dissolved oxygen, phosphorus, and pH). The exact measurement of water turbidity is a difficult process because many conditions affect the reading of turbidity. Although many researchers applied decomposition-based techniques for preprocessing, it is difficult to use these approaches in real estimation because the newly acquired data greatly affect the initial decomposed subsequent values. In this study, the threshold-based wavelet denoising method, as a data pre-processing, coupled with the deep learning models (i.e., ANN and ANFIS) was employed to enhance the performance of the water turbidity modeling. The results showed that deep learning techniques in temporal modeling of water turbidity have good accuracy and can be used with reasonable confidence. Also, data denoising increases the accuracy of deep learning methods in estimating the amount of water turbidity. ANFIS method is more accurate in both calibration and validation modes as well as in noisy and denoised conditions. Based on the results, data denoising in the ANN method has a more significant impact than in the ANFIS technique. For example, in Comb. 5, which is the best case, the improvement rate of the results in the ANN is 12% and in the ANFIS method is 4%. This could be due to the fuzzy system in handling uncertainties in the model.

水体浊度与五个主要水质参数（电导率、氮、溶解氧、磷和 pH 值）高度相关，是水质的关键指标。水浊度的精确测量是一个困难的过程，因为许多条件都会影响浊度的读数。尽管许多研究人员应用基于分解的技术进行预处理，但这些方法很难在实际估计中使用，因为新获取的数据极大地影响了初始分解的后续值。在本研究中，采用基于阈值的小波去噪方法作为数据预处理，结合深度学习模型（即ANN和ANFIS）来增强水浊度建模的性能。结果表明，深度学习技术在水浊度时间建模中具有良好的准确性，并且可以合理地使用。此外，数据去噪还提高了深度学习方法估计水浊度的准确性。ANFIS 方法在校准和验证模式以及噪声和去噪条件下都更加准确。根据结果​​，ANN 方法中的数据去噪比 ANFIS 技术中的数据去噪具有更显着的影响。例如，在梳子中。如图5所示，这是最好的情况，ANN中的结果的改进率为12％，ANFIS方法中的结果的改进率为4％。这可能是由于模糊系统处理模型中的不确定性所致。