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An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction
Engineering Applications of Computational Fluid Mechanics ( IF 5.9 ) Pub Date : 2021-01-28 , DOI: 10.1080/19942060.2020.1861987
Kangjie Sun, Mohammad Rajabtabar, Seyedehzahra Zahra Samadi, Mohammad Rezaie-Balf, Alireza Ghaemi, Shahab S. Band, Amir Mosavi

Monitoring the water contaminants is of utmost importance in water resource management. Prediction of the total dissolved solid (TDS) is particularly essential for water quality management and planning in the areas exposed to a mixture of pollutants. TDS primarily includes inorganic minerals and organic matters, and various salts and increasing the concentration of TDS causes the esthetic problems. The reflection of the pollutant burden of the aquatic system can remarkably determined by TDS magnitudes. This study focuses on the prediction of TDS and several biochemical parameters such as Na, Ca, HCO3, and Mg in a river system. To overcome nonstationarity, randomness, and nonlinearity of the TDS data, a multi-step supervised machine learning evolutionary algorithm (MSMLEA) is proposed to improve the model's performance at two gaging stations, namely Rig-Cheshmeh and Soleyman-Tangeh, in the Tajan River, Iran. In addition, a hybrid model that recruits intrinsic time-scale decomposition (ITD) for frequency resolution of the input data as well as a multivariate adaptive regression spline (MARS) were adopted. A novel metaheuristic optimization algorithm, crow search algorithm (CSA), was also implemented to compute the optimal parameter values for the MARS model. To validate the proposed hybrid model, standalone MARS, empirical mode decomposition (EMD)-based models, and hybrid ITD-MARS as well as a MARS-CSA were considered as the benchmark models. Results suggest the ITD-MARS-CSA outperforms other models.



中文翻译:

集成的机器学习,噪声抑制和基于总体的算法,可改善总溶解固体的预测

监测水污染物在水资源管理中至关重要。总溶解固体(TDS)的预测对于暴露于污染物混合物的区域的水质管理和规划尤为重要。TDS主要包括无机矿物质和有机物,各种盐和TDS浓度的增加会引起美学问题。水生系统污染物负担的反映可以通过TDS幅度来确定。这项研究着重于预测TDS以及河流系统中的一些生化参数,例如Na,Ca,HCO3和Mg。为了克服TDS数据的非平稳性,随机性和非线性,提出了一种多步监督机器学习进化算法(MSMLEA),以提高模型在两个测站的性能,分别是伊朗塔扬河上的里格·切什梅和索莱曼·唐格。此外,采用了一种混合模型,该模型募集了用于输入数据频率解析的内在时间尺度分解(ITD)以及多变量自适应回归样条(MARS)。还实现了一种新颖的元启发式优化算法,即乌鸦搜索算法(CSA),以计算MARS模型的最佳参数值。为了验证所提出的混合模型,将独立的MARS,基于经验模式分解(EMD)的模型,混合ITD-MARS以及MARS-CSA作为基准模型。结果表明,ITD-MARS-CSA的性能优于其他模型。采用了一种混合模型,该模型募集了固有时间尺度分解(ITD)来对输入数据进行频率解析,并且采用了多元自适应回归样条(MARS)。还实现了一种新颖的元启发式优化算法,即乌鸦搜索算法(CSA),以计算MARS模型的最佳参数值。为了验证所提出的混合模型,将独立的MARS,基于经验模式分解(EMD)的模型,混合ITD-MARS以及MARS-CSA作为基准模型。结果表明,ITD-MARS-CSA的性能优于其他模型。采用了一种混合模型,该模型募集了固有时间尺度分解(ITD)来对输入数据进行频率解析,并且采用了多元自适应回归样条(MARS)。还实现了一种新颖的元启发式优化算法,即乌鸦搜索算法(CSA),以计算MARS模型的最佳参数值。为了验证所提出的混合模型,将独立的MARS,基于经验模式分解(EMD)的模型,混合ITD-MARS以及MARS-CSA作为基准模型。结果表明,ITD-MARS-CSA的性能优于其他模型。独立的MARS,基于经验模式分解(EMD)的模型,混合ITD-MARS以及MARS-CSA被视为基准模型。结果表明,ITD-MARS-CSA的性能优于其他模型。独立的MARS,基于经验模式分解(EMD)的模型,混合ITD-MARS以及MARS-CSA被视为基准模型。结果表明,ITD-MARS-CSA的性能优于其他模型。

更新日期:2021-01-28
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