Conjugation of deep learning and de noising data methods for short-term water turbidity forecasting

IF 2.4 3区 环境科学与生态学 Q2 ENGINEERING, CIVIL Journal of Hydro-environment Research Pub Date : 2024-01-01 DOI:10.1016/j.jher.2023.12.002
Shahram Mousavi
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Abstract

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.

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深度学习和去噪数据方法在短期水体浊度预报中的共轭作用
由于水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,因此水浊度是一项重要的水质指标。精确测量水浊度是一个困难的过程,因为许多条件都会影响浊度的读数。虽然许多研究人员采用了基于分解的技术进行预处理,但由于新获取的数据会极大地影响初始分解后的数值,因此很难将这些方法用于实际估算。本研究采用基于阈值的小波去噪方法作为数据预处理,并结合深度学习模型(即 ANN 和 ANFIS)来提高水浊度建模的性能。结果表明,深度学习技术在水浊度时空建模中具有良好的准确性,可以在合理的可信度下使用。此外,数据去噪提高了深度学习方法估计水体浊度的准确性。ANFIS 方法在校准和验证模式下以及在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 技术的影响更大。例如,在 Comb.5 中,ANN 方法的结果改进率为 12%,而 ANFIS 方法的结果改进率为 4%。这可能是由于模糊系统在处理模型中的不确定性。
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来源期刊
Journal of Hydro-environment Research
Journal of Hydro-environment Research ENGINEERING, CIVIL-ENVIRONMENTAL SCIENCES
CiteScore
5.80
自引率
0.00%
发文量
34
审稿时长
98 days
期刊介绍: The journal aims to provide an international platform for the dissemination of research and engineering applications related to water and hydraulic problems in the Asia-Pacific region. The journal provides a wide distribution at affordable subscription rate, as well as a rapid reviewing and publication time. The journal particularly encourages papers from young researchers. Papers that require extensive language editing, qualify for editorial assistance with American Journal Experts, a Language Editing Company that Elsevier recommends. Authors submitting to this journal are entitled to a 10% discount.
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