Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran

Barzegar, Rahim; Adamowski, Jan; Moghaddam, Asghar Asghari

doi:10.1007/s00477-016-1213-y

Cited by 151 publications

(58 citation statements)

References 84 publications

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“…After this, some new models for water quality prediction have been proposed, which integrates different non-mechanism models, such as the neural networks model, fuzzy model, and wavelet model [13][14][15]. In addition, there are also some joint models that combine hydrodynamics and water quality simulation tests [16,17]. However, most non-mechanism models require that the variables obey the same marginal distribution or that these variables need to be transformed to the same distribution, which not only leads to the loss of the original data information, but it also does not conform to the fact that variables, such as discharge and water quality, obey different marginal distributions.…”

Section: Introductionmentioning

confidence: 99%

Combined Exceedance Probability Assessment of Water Quality Indicators Based on Multivariate Joint Probability Distribution in Urban Rivers

Liu¹,

Cheng²,

Zhang³

et al. 2018

Water

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Abstract:Discharge and water quality are two important attributes of rivers, although the joint response relationship between discharge and multiple water quality indicators is not clear. In this paper, the joint probability distributions are established by copula functions to reveal the statistical characteristics and occurrence probability of different combinations of discharge and multiple water quality indicators. Based on the data of discharge, ammonia nitrogen content index (NH 4 + ) and permanganate index (COD Mn ) in the Xiaoqing River in Jinan, we first tested the joint change-point with the data from 1980-2016, before we focused on analyzing the data after the change-point and established the multivariate joint probability distributions. The results show that the Gaussian copula is more suitable for describing the joint distribution of discharge and water quality, while the year of 2005 is a joint change-point of water quantity and quality. Furthermore, it is more reasonable to use the trivariate joint probability distribution as compared to the bivariate distributions to reflect the exceedance probability of water quality combination events under different discharge conditions. The research results can provide technical support for the water quality management of urban rivers.

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Section: Introductionmentioning

confidence: 99%

Combined Exceedance Probability Assessment of Water Quality Indicators Based on Multivariate Joint Probability Distribution in Urban Rivers

Liu¹,

Cheng²,

Zhang³

et al. 2018

Water

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“…6. A detailed description of the hydrogeology of the Tabriz plain is presented in works by Asghari Asghri Moghaddam (1991), Barzegar (2014), Barzegar et al (2015Barzegar et al ( , 2016a, Barzegar and Asghari Moghaddam (2016). The general direction of groundwater flow follows the topography of the area, mainly from northeast to southwest and east to west (Fig.…”

Section: Geology and Hydrogeologymentioning

confidence: 99%

“…Low EC (sample 1) is located in the recharge areas originating from coarse-grained alluvial fans, while individual elevated EC values (e.g., sample 7) should have probably variable origins, such as the low depth of water table and subsequent increased evaporation, the elevated salinity of Aji-Chay River (average EC = 9500 lS cm -1 ) which is hydraulically connected to groundwater (Barzegar et al 2016a) and, the excessive use of salts from industrial tanning activities. Spatial distribution of the EC is shown in Fig.…”

Section: Spatial Variability Of Groundwater Quality Parametersmentioning

confidence: 99%

“…The Aji-Chay, Gomanab-Chay and Sinekh-Chay Rivers cross the Upper Red formations, therefore these rivers transport fine suspended solid materials during the higher discharges and saline water during the lower discharge periods (Barzegar et al 2016a). The Aji-Chay River deposits are composed by alluvial terraces in the central parts of the plain and divide the plain in hydrogeological terms, into two southern and northern parts (Barzegar et al 2015).…”

Section: Geology and Hydrogeologymentioning

confidence: 99%

“…The maximum and minimum humidity are 69 and 32%, respectively. The plain lies within the cold and dry climate according to the Emberger (1930) classification (Barzegar 2014;Barzegar et al 2015Barzegar et al , 2016a.…”

Section: Study Areamentioning

confidence: 99%

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Hydrogeochemical features of groundwater resources in Tabriz plain, northwest of Iran

Barzegar

Moghaddam

Tziritis³

2017

Appl Water Sci

Self Cite

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The present study seeks to evaluate the hydrogeochemistry of Tabriz plain in NW Iran, through major ion chemistry and their spatial variations. In order to accomplish these, groundwater sampling from 30 shallow and deep wells in the plain were carried out in July 2012. The water samples were analyzed for various physicochemical parameters such as pH, EC, Na ? , Ca 2? , K ? , Mg 2? , Cl -, CO 3 2-, HCO 3 -, SO 4 2-and NO 3 -. Chadha's diagram demonstrates that most of the groundwaters belonged to the Na-Cl and mixed Ca-Mg-Cl hydrochemical facies. The concentrations of some major ions in groundwater are above the permissible limit for drinking and domestic purposes except for a few locations. The results of saturation index computation show that dissolution of gypsum, anhydrite, halite and silicate minerals occurs frequently across the study area, whereas the groundwater is supersaturated with regard to calcite and dolomite. Cross-plots show that weathering and dissolution of different rocks and minerals, ion exchange, reverse ion exchange and anthropogenic activities, especially agricultural activities, are effective in hydrogeochemistry of the study area.

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Multi‐Scale Fuzzy Inference System for Influent Characteristic Prediction of Wastewater Treatment

Cheng

Li²,

Bai

et al. 2018

CLEAN Soil Air Water

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Accurate influent characteristic prediction is vital to maintain the stable performance of wastewater treatment processes. In this work, an associated approach based on the wavelet packet decomposition (WPD) and adaptive network‐based fuzzy inference system (ANFIS) is proposed to address this issue. In this method, the WPD is first adopted to decompose the historical data of the influent characteristic into wavelet coefficients in different scales. The time sub‐series, which are obtained with a single branch reconstruction of the wavelet coefficients in each scale, are then utilized to build the ANFIS regression model. The predicted sub‐results in each scale are finally summarized into an eventual predicted result. Moreover, a particle swarm optimization (PSO) algorithm is employed to acquire the optimal parameters of the multi‐scale ANFIS, and chaos theory is utilized to determine the input variables of the multi‐scale ANFIS. The reported approach is investigated by the influent characteristic data, including the chemical oxygen and biochemical oxygen demands from a wastewater treatment plant (WTP) in southwest of China. Two peer models are introduced for a comparison study. The results show that the developed approach has superior performance in terms of the mean absolute error (3.346 and 1.384), mean absolute percentage error (1.804% and 1.800%), root mean square error (3.988 and 1.788), and correlation coefficient (0.960 and 0.964), and can accurately predict the influent characteristic of the WTP.

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Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran

Cited by 151 publications

References 84 publications

Combined Exceedance Probability Assessment of Water Quality Indicators Based on Multivariate Joint Probability Distribution in Urban Rivers

Combined Exceedance Probability Assessment of Water Quality Indicators Based on Multivariate Joint Probability Distribution in Urban Rivers

Hydrogeochemical features of groundwater resources in Tabriz plain, northwest of Iran

Multi‐Scale Fuzzy Inference System for Influent Characteristic Prediction of Wastewater Treatment

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