A Comparative Study Of Ann For Predicting Nitrate Concentration In Groundwater Wells In The Southern Area Of Gaza Strip

Zaqoot, Hossam Adel; Hamada, Mazen; Miqdad, Shady

doi:10.1080/08839514.2018.1506970

Cited by 20 publications

(4 citation statements)

References 29 publications

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“…ANN models can deal with complex systems such as groundwater. The ANN is a nonlinear model designed to deal with large datasets with multiple variables as input [18][19][20]. Multi-layer perceptron (MLP) is the widely used ANN architectural design that has been used abundantly in hydrological modeling to predict and forecast the dataset [21][22][23][24][25][26].…”

Section: Artificial Neural Network (Ann) Model Design and Characteris...mentioning

confidence: 99%

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Farooq

Zafar

Raheem

et al. 2022

Water

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Estimating groundwater quality parameters through conventional methods is time-consuming through laboratory measurements for megacities. There is a need to develop models that can help decision-makers make policies for sustainable groundwater reserves. The current study compared the efficiency of multivariate linear regressions (MLR) and artificial neural network (ANN) models in the prediction of groundwater parameters for total dissolved solids (TDS) for three sub-divisions in Lahore, Pakistan. The data for this study were collected every quarter of a year for six years. ANN was applied to investigate the feasibility of feedforward, backpropagation neural networks with three training functions T-BR (Bayesian regularization backpropagation), T-LM (Levenberg–Marquardt backpropagation), and T-SCG (scaled conjugate backpropagation). Two activation functions were used to analyze the performance of algorithmic training functions, i.e., Logsig and Tanh. Input parameters of pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), chloride (Cl−), and sulfate (SO42−) was used to predict TDS as an output parameter. The computed values of TDS by ANN and MLR were in close agreement with their respective measured values. Comparative analysis of ANN and MLR showed that TDS root means square error (RMSE) for city sub-division and Pearson’s coefficient of correlation (r) for ANN and MLR were 2.9% and 0.981 and 4.5% and 0.978, respectively. Similarly, for the Farrukhabad sub-division, RMSE and r for ANN were 4.9% and 0.952, while RMSE and r for MLR were 5.5% and 0.941, respectively. For the Shahadra sub-division, RMSE was 10.8%, r was 0.869 for ANN, RMSE was 11.3%, and r was 0.860 for MLR. The results exhibited that the ANN model showed less error in results than MLR. Therefore, ANN can be employed successfully as a groundwater quality prediction tool for TDS assessment.

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Section: Artificial Neural Network (Ann) Model Design and Characteris...mentioning

confidence: 99%

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Farooq

Zafar

Raheem

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…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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“…Applied models Performance metrics [11] Sangamon River, USA BPNN, RBFNN RMSE [12] Harran Plain, Turkey MLP with BP and Levenberg-Marquardt R-value, MSE [13] Kutahya, Turkey BPNN MSE, MAPE [14] Kadava River basin, Nashik, Maharashtra, India MLP with Levenberg-Marquardt R 2 , RMSE, MARE [15] Shandong, China BPNN R-value [16] Northern part of Iran BPNN, RBFNN MSE [17] Central Valley, California BRT, ANN, Bayesian networks R 2 [18] Bethune, France MLP with BP R-value [19] African continent RFR, MLR R 2 [20] Marvdasht watershed, Iran SVM, Cubist, random forest, Bayesian-ANN R 2 , MAE, RMSE, Nash-Sutcliffe efficiency (NSE) [21] Gaza Strip, Palestine MLP, RBFNN RMSE, R-value, MAE [22] Gaza Strip, Palestine MLP with BP and Levenberg-Marquardt, SVM R-value, MAPE, NSE [23] Gaza Strip, Palestine MLP, RBF, GRNN, and linear networks. R-value [24] Arak plain, Iran SVM RMSE…”

Section: Reference Case Study Regionmentioning

confidence: 99%

The Performance of Artificial Neural Network Approaches to Estimate the Nitrate Concentration in Groundwater

Çoban¹

2021

European Journal of Science and Technology

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The estimation of the pollution concentration in groundwater is important, since it is one of the key resources of water supply. Nitrate (NO3-N) is one of the well-known indicator parameters in groundwater pollution. Using historical data, artificial neural networks can be utilized to estimate the nitrate concentration in groundwater. In this study, a sample dataset, which is derived from a survey analysis in the literature, is used to estimate the nitrate concentration of groundwater (i.e., target parameter) with respect to six different well characteristics (i.e., input parameters). The effect of different hydrogeological parameters of the wells on the nitrate concentration is focused for the first time in this study. The performance of two different ANN approaches, namely BPNN and GRNN, is evaluated comparatively by means of their regression performances. Considering regression results of ANN models, it can be concluded that the GRNN (R=0.99) algorithm works slightly better than the BPNN (R=0.98) algorithm with this dataset. Correlation results indicate that the most important characteristics of the wells to estimate the nitrate pollution are the well depth, depth below water table, clay above screen, and depth to well screen, respectively. Moreover, all these characteristics are inversely related to nitrate concentration of the well.

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A Comparative Study Of Ann For Predicting Nitrate Concentration In Groundwater Wells In The Southern Area Of Gaza Strip

Cited by 20 publications

References 29 publications

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

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

The Performance of Artificial Neural Network Approaches to Estimate the Nitrate Concentration in Groundwater

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