Groundwater Quality Modeling with a Small Data Set

Sakizadeh, Mohamad; Malian, A. Husni; Ahmadpour, Eisa

doi:10.1111/gwat.12317

Cited by 8 publications

(3 citation statements)

References 19 publications

(26 reference statements)

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“…ANNs models the approximation and details component from the discrete wavelet transformation (see Figure 4). Dimensionality reduction methods such as PCA can reduce the dimension of the input data space to prevent redundancy [73]. Then, ANNs models some aggregative indices obtained by PCA (see Figure 4).…”

Section: Hybrid Architecturesmentioning

confidence: 99%

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A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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Section: Hybrid Architecturesmentioning

confidence: 99%

“…Results showed that the hybrid model, namely chaos theory-PCA-ANN, had high prediction accuracy. Sakizadeh et al [73] applied early stopping which is fit for small networks and datasets to determine the model structure.…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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“…Classification is a supervised learning process. [13][14][15][16][17][18][19][20][21][22][23][24][25]. Actually, the ANN models can handle crisp data, but some of the classification problems need to process the data with uncertainty along with the crisp data.…”

Section: Applications Of Ann and Fuzzy Logic In Groundwater Classificationmentioning

confidence: 99%

An Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data

Kiran¹,

Chaparala²,

Radhika³

2021

IJDNE

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The groundwater for aquatic purposes must be assessed prior to its consumption. Huge number of conventional methods are existing for assessing the quality of groundwater. The water quality index is one of the important conventional methods to assess the groundwater quality. But the conventional methods alone are not enough to assess groundwater quality as well as classify based on its purity. In this paper, we propose an enhanced weight update method for Simplified Fuzzy Adaptive Resonance Theory model to classify the groundwater quality depending on the relative weights of the groundwater quality parameters. Finding the optimal weights is the key to achieve better accuracy of the model, most of the nonlinear models fails to exhibit good accuracy if they fail to learn the optimal weights in the learning process. The aim of the work is to find the good fit between the predicted and the actual groundwater quality grades by identifying the optimal weights of the network by the enhanced weight update method. The Simplified Fuzzy Adaptive Resonance Theory map with the enhanced weight update method performance is justified by comparing it with the Simplified Fuzzy Adaptive Resonance Theory Map. The enhanced weight update method improves the accuracy of the Simplified Fuzzy Adaptive Resonance Theory Map in classifying and predicting the groundwater quality.

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A new four-step hierarchy method for combined assessment of groundwater quality and pollution

Zhu

Ren

Liu

2017

Environ Monit Assess

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A new four-step hierarchy method was constructed and applied to evaluate the groundwater quality and pollution of the Dagujia River Basin. The assessment index system is divided into four types: field test indices, common inorganic chemical indices, inorganic toxicology indices, and trace organic indices. Background values of common inorganic chemical indices and inorganic toxicology indices were estimated with the cumulative-probability curve method, and the results showed that the background values of Mg (51.1 mg L), total hardness (TH) (509.4 mg L), and NO (182.4 mg L) are all higher than the corresponding grade III values of Quality Standard for Groundwater, indicating that they were poor indicators and therefore were not included in the groundwater quality assessment. The quality assessment results displayed that the field test indices were mainly classified as grade II, accounting for 60.87% of wells sampled. The indices of common inorganic chemical and inorganic toxicology were both mostly in the range of grade III, whereas the trace organic indices were predominantly classified as grade I. The variabilities and excess ratios of the indices were also calculated and evaluated. Spatial distributions showed that the groundwater with poor quality indices was mainly located in the northeast of the basin, which was well-connected with seawater intrusion. Additionally, the pollution assessment revealed that groundwater in well 44 was classified as "moderately polluted," wells 5 and 8 were "lightly polluted," and other wells were classified as "unpolluted."

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Groundwater Quality Modeling with a Small Data Set

Cited by 8 publications

References 19 publications

A Review of the Artificial Neural Network Models for Water Quality Prediction

A Review of the Artificial Neural Network Models for Water Quality Prediction

An Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data

A new four-step hierarchy method for combined assessment of groundwater quality and pollution

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