Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Jeihouni, Mehrdad; Delirhasannia, Reza; Alavipanah, Seyed Kazem; Shahabi, Mahmoud; Samadianfard, Saeed

doi:10.15233/gfz.2015.32.9

Cited by 11 publications

(8 citation statements)

References 51 publications

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“…So, the common practice in the mentioned situations is to use mathematical, empirical or recently data-driven techniques, which have been established on the basis of measured meteorological parameters, to have precise estimations of actual solar radiation (Ozgoren, Bilgili, & Sahin, 2012;Sun, Zhao, Zeng, & Yan, 2015). Data-driven techniques have the numerous applications in hydrological engineering and contemporary real-life problems (Chau, 2017;Fotovatikhah et al, 2018;Jeihouni, Delirhasannia, Alavipanah, Shahabi, & Samadianfard, 2015;Moazenzadeh, Mohammadi, Shamshirband, & Chau, 2018;Samadianfard, Delirhasannia, Kisi, & Agirre-Basurko, 2013;Samadianfard, Nazemi, & Sadraddini, 2014;Samadianfard, Sattari, Kisi, & Kazemi, 2014;Taormina, Chau, & Sivakumar, 2015;Wu & Chau, 2011). So, the researchers attempt to develop accurate predictor models of solar radiation in various time scales that is a crucial issue of related solar energy sciences.…”

Section: Introductionmentioning

confidence: 99%

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Samadianfard

Majnooni-Heris

Qasem

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Solar radiation, moisture and temperature are the most vital meteorological variables which affect plant growth. Due to the fact that the global solar radiation (GSR) is scarcely gauged at meteorological stations in developing countries, it is commonly estimated by data-driven techniques or by empirical equations. In this study, support vector regression (SVR), model trees (MT), gene expression programming (GEP) and adaptive neuro-fuzzy inference system (ANFIS) and several empirical equations were applied to assess the relations between GSR and several meteorological variables including minimum temperature (T min ), maximum temperature (T max ), relative humidity (RH), sunshine hours (n), maximum sunshine hours (N), corrected clear-sky solar irradiation (ICSKY), day of year (DOY) and extra-terrestrial radiation (R a ). For this purpose, the daily GSR measured from the beginning of 2011 to the end of 2013 at Tabriz synoptic station, which is located in semi-arid regions of Iran, were used. A direct strong relationship was observed to exist between the GSR and n. For evaluating the performances of studied techniques, three different statistical indicators were used namely root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC). Additionally, a Taylor diagram was utilized to test the similarity between the observed and predicted GSR values. Results indicated that the SVR-6 with input parameters of R a , RH, T min , T max , n/N had better accuracy in predicting GSR with RMSE of 1.656, MAE of 0.990, CC of 0.980 and WI of 0.990 than the other models. Moreover, MT-6 ranked as the second best model in the prediction of GSR values. As an interesting point, studied empirical equations had lower accuracies comparing with the SVR, GEP, MT and ANFIS methods. For instance, GSR values were computed by Angstrom and Prescott equation, as the best empirical equation, with RMSE of 1.786, MAE of 1.156, CC of 0.977 and WI of 0.988. Conclusively, results from the current study proved that the SVR provided reasonable trends for GSR modeling at Tabriz synoptic station. Furthermore, MT models with linear equations can be implemented with a high degree of simplicity and acceptable precision in GSR estimation.

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Section: Introductionmentioning

confidence: 99%

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Samadianfard

Majnooni-Heris

Qasem

et al. 2019

Engineering Applications of Computational Fluid Mechanics

Self Cite

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“…The ability of ML models to model groundwater salinity has been demonstrated via the establishment of a linear or non-linear relationship between water salinity and its control parameters (such as water table, evaporation, and distance to saltwater bodies) and using those relationships for the prediction of water salinity for regions with unavailable data points 39 , 40 . Various versions of ML models have been reported in the literature, such as artificial neural network (ANN) 41 – 45 , support vector machine (SVM) 46 – 48 , adaptive neuro-fuzzy inference system (ANFIS) 49 , 50 , ensemble ML models 38 , 51 , 52 , group method of data handling (GMDH) 53 , and Gaussian process scheme 54 . The significant limitations associated with predictive ML models (1) the need for adequate input variables to explain the target data that may not be available everywhere 55 , 56 , (2) the influence of well excessive pumping 57 , 58 , (3) the reliability of the learning process of the predictive model where essential hyperparameters are optimized 59 , 60 , (4) coupled ML models where a pre-processing technique was integrated for data time series decomposition 61 , 62 .…”

Section: Introductionmentioning

confidence: 99%

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Jamei

Karbasi

Malik

et al. 2022

Sci Rep

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The rising salinity trend in the country’s coastal groundwater has reached an alarming rate due to unplanned use of groundwater in agriculture and seawater seeping into the underground due to sea-level rise caused by global warming. Therefore, assessing salinity is crucial for the status of safe groundwater in coastal aquifers. In this research, a rigorous hybrid neurocomputing approach comprised of an Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with a new meta-heuristic optimization algorithm, namely Aquila optimization (AO) and the Boruta-Random forest feature selection (FS) was developed for estimating the salinity of multi-aquifers in coastal regions of Bangladesh. In this regard, 539 data samples, including ten water quality indices, were collected to provide the predictive model. Moreover, the individual ANFIS, Slime Mould Algorithm (SMA), and Ant Colony Optimization for Continuous Domains (ACOR) coupled with ANFIS (i.e., ANFIS-SMA and ANFIS-ACOR) and LASSO regression (Lasso-Reg) schemes were examined to compare with the primary model. Several goodness-of-fit indices, such as correlation coefficient (R), the root mean squared error (RMSE), and Kling-Gupta efficiency (KGE) were used to validate the robustness of the predictive models. Here, the Boruta-Random Forest (B-RF), as a new robust tree-based FS, was adopted to identify the most significant candidate inputs and effective input combinations to reduce the computational cost and time of the modeling. The outcomes of four selected input combinations ascertained that the ANFIS-OA regarding the best accuracy in terms of (R = 0.9450, RMSE = 1.1253 ppm, and KGE = 0.9146) outperformed the ANFIS-SMA (R = 0.9406, RMSE = 1.1534 ppm, and KGE = 0.8793), ANFIS-ACOR (R = 0.9402, RMSE = 1.1388 ppm, and KGE = 0.8653), Lasso-Reg (R = 0.9358), and ANFIS (R = 0.9306) models. Besides, the first candidate input combination (C1) by three inputs, including Cl− (mg/l), Mg2+ (mg/l), Na+ (mg/l), yielded the best accuracy among all alternatives, implying the role importance of (B-RF) feature selection. Finally, the spatial salinity distribution assessment in the study area ascertained the high predictability potential of the ANFIS-OA hybrid with B-RF feature selection compared to other paradigms. The most important novelty of this research is using a robust framework comprised of the non-linear data filtering technique and a new hybrid neuro-computing approach, which can be considered as a reliable tool to assess water salinity in coastal aquifers.

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“…Chowdhury et al [16] compared the ANN and ordinary kriging (OK) techniques for spatial estimation of the As concentrations in Bangladesh, and pointed out that a highly nonlinear pattern machine learning technique in the form of an ANN model can yield more accurate results than OK under the same set of constraints. Jeihouni et al [19] used the OK and two AI methods, namely, ANN and the adaptive neuro-fuzzy inference system (ANFIS), to spatially assess the electrical conductivity of groundwater. Their results indicated that ANFIS provides the best prediction accuracy with a root mean squared error (RMSE) value of 1.69 dS.m, whereas the RMSEs are 1.79 dS.m and 2.14 dS.m for ANN and OK, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain

Liang

Sun

Suk

et al. 2021

IJERPH

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Groundwater resources are abundant and widely used in Taiwan’s Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization’s standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R2) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.

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Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran)

Cited by 11 publications

References 51 publications

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Daily global solar radiation modeling using data-driven techniques and empirical equations in a semi-arid climate

Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan’s Lanyang Plain

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