Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin

Ghavidel, Sarvin Zaman Zad; Montaseri, Majid

doi:10.1007/s00477-014-0899-y

Cited by 61 publications

(16 citation statements)

References 57 publications

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“…Clustering of available data is essential for developing a fuzzy modeling system. Fuzzy c-means (FCM) and subtractive clustering (SC) are two powerful fuzzy clustering techniques, which could be used for the construction of Mamdani and Sugeno models, respectively (Ghavidel and Montaseri 2014). Each of the clusters refers to a membership function for generating the fuzzy ''if-then'' rules.…”

Section: Fuzzy Logicmentioning

confidence: 99%

See 1 more Smart Citation

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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Vulnerability maps are designed to show areas of greatest potential for groundwater contamination on the basis of hydrogeological conditions and human impacts. The objective of this research is (1) to assess the groundwater vulnerability using DRASTIC method and (2) to improve the DRASTIC method for evaluation of groundwater contamination risk using AI methods, such as ANN, SFL, MFL, NF and SCMAI approaches. This optimization method is illustrated using a case study. For this purpose, DRASTIC model is developed using seven parameters. For validating the contamination risk assessment, a total of 243 groundwater samples were collected from different aquifer types of the study area to analyze NO À 3 concentration. To develop AI and CMAI models, 243 data points are divided in two sets; training and validation based on cross validation approach. The calculated vulnerability indices from the DRASTIC method are corrected by the NO À 3 data used in the training step. The input data of the AI models include seven parameters of DRASTIC method. However, the output is the corrected vulnerability index using NO À 3 concentration data from the study area, which is called groundwater contamination risk. In other words, there is some target value (known output) which is estimated by some formula from DRASTIC vulnerability and NO À 3 concentration values. After model training, the AI models are verified by the second NO À 3 concentration dataset. The results revealed that NF and SFL produced acceptable performance while ANN and MFL had poor prediction. A supervised committee machine artificial intelligent (SCMAI), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of vulnerability. The performance of SCMAI was also compared to those of the simple averaging and weighted averaging committee machine intelligent (CMI) methods. As a result, the SCMAI model produced reliable estimates of groundwater contamination risk.

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Section: Fuzzy Logicmentioning

confidence: 99%

“…Each neuron receives signals from the neurons of the previous layer weighted by the weighted connections between neurons except in the input layer. Neurons then produce an output signal by passing the summed signal through an activation function(Maqsood et al 2005;Ghavidel and Montaseri 2014).…”

mentioning

confidence: 99%

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

Barzegar

Moghaddam

Baghban

2015

Stoch Environ Res Risk Assess

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“…It is undeniable that there are a large number of variables which have significant influences on the TDS estimation. For example, Ghavidel and Montaseri (2014) selected Bicarbonate (HCO3), Calcium (Ca), Sodium (Na), Magnesium (Mg), and river discharge as input variables to estimate TDS. Asadollahfardi et al (2016) selected HCO3, pH, Na, Mg, carbonate (CO3), Ca, and chloride (Cl) as input variables for the TDS study.…”

Section: Case Study and Sampling Locationsmentioning

confidence: 99%

“…Abudu et al (2012) applied ANN, transfer functionnoise, and Autoregressive Integrated Moving Average (ARIMA) techniques for the monthly prediction of TDS content in the Rio Grande in El Paso, Texas. Ghavidel and Montaseri (2014) employed ANN, GEP, and ANFIS with grid partition as well as ANFIS with subtractive clustering to predict TDS values of the Zarinehroud basin, Iran. In a sequence, Khaki et al (2015) evaluated the ability of ANN and ANFIS for the TDS estimation in the Langat Basin, Malaysia.…”

Section: Introductionmentioning

confidence: 99%

An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Sun

Rajabtabar

Phillips

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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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 (MSM-LEA) 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.

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“…Water quality constituents have also been predicted using data-driven models in many studies (Preis and Ostfeld 2008;Solomatine et al 2007;Ghavidel and Montaseri 2014;Burchard-Levine et al 2014). They are effective in building knowledge-driven simulations, that are capable of extracting different system states when the nature of complex relationships is unknown, or when the available models are inadequate (Solomatine et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Case Study of the Chicago River Watershed: Physical Modeling vs Data-driven Modeling of an Urban Watershed

Mahdi

Elhadi

Pagilla

2017

JWMM

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We developed a water quality model for the highly urbanized Chicago River watershed based on hydrologic simulation using BASINS/HSPF. Appropriate consideration was given to the effective impervious area (EIA). The 5 y water quality simulation resulted in finding total nitrates loadings at both point and nonpoint sources. However, it is always useful to have modeling alternatives to validate the simulation results of a physically based model with a data-driven one. Data-driven modeling has gained a lot of attention in recent decades in both hydrology and water resources research. While physically based models require the description of system inputs, physical laws and boundary and initial conditions, a data-driven model simply extracts knowledge from a large amount of data with only a limited number of assumptions about the physical behaviour of the system. For this case study, both data-driven and physical models were considered to simulate total nitrates. Comparing the performance of the two modeling approaches, the data-driven models show better performance. RMSE for regression models showed an increase in prediction performance of up to 10.7 %. Data-driven models require fewer inputs and can be deployed anywhere in the watershed, while physical models require extensive data inputs and can only be applied to the specific watershed outlets selected in the simulation.These arguments suggest the complementary use of both physical and data-driven models. The physical model can be a planning tool whenever significant physical change takes place in the watershed. The data-driven model can be an operating tool that can be periodically used to inspect the watershed water quality parameters, especially if TMDL and WQS are established for the watershed.

show abstract

Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin

Cited by 61 publications

References 57 publications

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

A supervised committee machine artificial intelligent for improving DRASTIC method to assess groundwater contamination risk: a case study from Tabriz plain aquifer, Iran

An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Case Study of the Chicago River Watershed: Physical Modeling vs Data-driven Modeling of an Urban Watershed

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