Prediction of electrical conductivity using ANN and MLR: a case study from Turkey

Keskin, Tülay Ekemen; Özler, Emre; Şander, Emrah; Düğenci, Muharrem; Ahmed, Mohammed Yadgar

doi:10.1007/s11600-020-00424-1

Cited by 19 publications

(5 citation statements)

References 12 publications

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Section: Comparison Between Two Developed Modelssupporting

confidence: 92%

“…Our results agree with those reported by other works that found ANN and MLR models suitable for the prediction of parameters related to salinity and groundwater quality [14,16,19,20]. Ekemen Keskin et al [14] also concluded that the ANN-based model was slightly more accurate than the MLR for irrigation groundwater management in a region of Turkey. Singh et al [19] also found the ANN model more accurate than MLR for predicting various salinity parameters in the groundwater of Punjab (India), at least during the post-monsoon season.…”

Section: Comparison Between Two Developed Modelssupporting

confidence: 92%

“…Barzegar and Asghari Moghaddam [13] proposed a committee neural network (CNN) developed by the combination of the salinity results from three individual ANN models (multi-layer perceptron (MLP), radial basis function (RBFNN), and generalized regression (GRNN)) for predicting the groundwater EC of the Tabriz plain confined aquifer. Ekemn Keskin et al [14] forecasted EC of groundwater, from some areas in Turkey, using an artificial neural network (ANN) trained with backpropagation and a statistical model (MLR (multiple linear regression)) and reported higher performance for the ANN model. Kheradpisheh et al [15] developed a feed-forward backpropagation neural network (FNN-BP) model to estimate EC together with Cl − and SO 4 2− in groundwater samples from Bahabad plain in Iran.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Mateo,

Más-López,

García-del-Toro

et al. 2024

Agronomy

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Groundwater is a crucial water resource, particularly in regions with intensive agriculture and a semi-arid climate, such as Campo de Cartagena (Murcia, Spain). Groundwater salinity in the area can be attributed to hydrogeological characteristics, irrigation return water, or even marine intrusion and communication between aquifers. The management of these waters is essential to maintain sustainable agriculture in the area. Therefore, two groundwater salinity prediction models were developed, a backpropagation artificial neural network (ANN) model and a multiple linear regression (MLR) model, based on EC (electrical conductivity) data obtained from official information sources. The data used were the bicarbonate, calcium, chloride, magnesium, nitrate, potassium, sodium, and sulphate concentrations, as well as EC, pH, and temperature, of 495 water samples from 38 sampling stations between 2000 and 2023. Variables with the least influence on the model were discarded in a previous statistical analysis. Based on seven evaluation metrics (RMSE, MAE, R2, MPE, MBE, SSE, and AARD), the ANN model showed a sligntly better accuracy in predicting EC compared to the MLR model. As a result, the ANN model, together with crop tolerance to EC, may be an effective tool for groundwater irrigation management in these areas.

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Section: Comparison Between Two Developed Modelssupporting

confidence: 92%

Section: Comparison Between Two Developed Modelssupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Mateo,

Más-López,

García-del-Toro

et al. 2024

Agronomy

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“…MLR is a statistical technique used in predictive modeling to analyze the relationship between a dependent variable and multiple independent variables [37]. Unlike simple linear regression, which involves a single predictor, multiple linear regression considers two or more predictors [38].…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Treated Wastewater Assessment to Optimize Agricultural Water Reuse in Al-Qatif Region Saudi Arabia Using Hybrid Machine Learning Techniques

Shah,

Yassin,

Abba

et al. 2024

Preprint

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In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

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“…ANN toolbox in MATLAB package 2018a was used in this study to develop the feed-forward back propagation (FFBP) ANN model with 3-7-1. BP is the most commonly applied powerful learning algorithms in multilayer networks (Hajihassani et al 2014;Ekemen Keskin et al 2020). The predictive input parameters ρd, BTS, and Is(50) were allocated to an input layer composed of three neurons to predict UCS of the output layer.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Predictive Modeling of Uniaxial Compressive Strength of Rocks for Protecting Environment Using Artificial Neural Network

Shahani

Zheng

2021

Preprint

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Sedimentary rocks provide information on previous environments on the surface of the earth. As a result, they are the principal narrators of former climate, life, and important events on the surface of the earth. Complexity and expensiveness of direct destructive laboratory tests are adversely affects the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established artificial neural network (ANN) approach to predict uniaxial compressive strength (UCS) in MPa of soft sedimentary rocks using different input parameters i.e. dry density (ρd) in g/cm3; Brazilian tensile strength (BTS) in MPa; point load index (Is(50)) in MPa. The developed ANN models M1, M2 and M3 were divided into the overall dataset; 70% training dataset and 30% testing dataset; and 60% training dataset and 40% testing dataset respectively. In addition, multiple linear regression (MLR) was performed to compare with the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M3 ANN model with the highest coefficient of correlation (R2), the smallest root mean squared error (RMSE), the highest variance accounts for (VAF) and reliable a10-index was 0.99, 0.00060, 0.99 and 0.99 respectively at the testing dataset revealing ideal results and proposed as the best-fit prediction model for UCS of soft sedimentary rocks at the Thar Coalfield, Pakistan, among other developed models in this study. Moreover, by performing sensitivity analysis, it was determined that the BTS and Is(50) were the most influential parameters in predicting UCS.

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Prediction of electrical conductivity using ANN and MLR: a case study from Turkey

Cited by 19 publications

References 12 publications

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Treated Wastewater Assessment to Optimize Agricultural Water Reuse in Al-Qatif Region Saudi Arabia Using Hybrid Machine Learning Techniques

Predictive Modeling of Uniaxial Compressive Strength of Rocks for Protecting Environment Using Artificial Neural Network

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