Modeling groundwater quality by using hybrid intelligent and geostatistical methods

Maroufpoor, Saman; Jalali, Mohammadnabi; Nikmehr, Saman; Shiri, Naser; Shiri, Jalal; Maroufpoor, Eisa

doi:10.1007/s11356-020-09188-z

Cited by 21 publications

(4 citation statements)

References 71 publications

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“…The reason for the better performance of ANN may be because of its ability to learn and recognize linear, nonlinear and complex relations between input output variables (Mallik et al 2022). It has been by El Bilali and Taleb (2020), Yıldız and Karakuş (2020), Ahmed et al (2019) The result of the present study is in agreement with the findings by M'nassri et al (2022), Maroufpoor et al (2020). The methodology used in this study improves the prediction of irrigation water quality parameters.…”

Section: Comparison Of Ann and Anfis Modelssupporting

confidence: 92%

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

Taşan

2022

Appl Water Sci

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Groundwater is one of the most important natural resources in the world and is widely used for irrigation purposes. Groundwater quality is affected by various natural heterogeneities and anthropogenic activities. Consequently, monitoring groundwater quality and assessing its suitability are crucial for sustainable agricultural irrigation. In this study, the suitability of groundwater for irrigation was determined by using sodium adsorption ratio (SAR), residual sodium carbonate (RSC), Kelly index (KI), percentage of sodium (Na%), magnesium ratio (MR), potential salinity (PS) and permeability index (PI). The groundwater samples were collected and analyzed from 37 different sampling stations for this purpose. Along with suitability analysis, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were used to predict irrigation water quality parameters. The models were evaluated by comparing the measured values and the predicted values using the statistical criteria [coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NS)]. In the estimation of all irrigation water quality parameters, the ANN model has performed much higher compared with the ANFIS model. Spatial distribution maps were generated for measured and ANN model-estimated irrigation water quality indices using the IDW interpolation method. Spatial distributions of groundwater quality indices revealed that MR was higher than the allowable limits in most of the study areas and the other quality criteria were within the permissible limits. It has been determined that the interpolation maps obtained as a result of artificial intelligence methods have appropriate sensitivity when compared with the observed maps. Based on the present findings, ANN models could be used as an efficient tool for estimating groundwater quality indices in unsampled sections of the study area and the other regions with similar conditions.

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Section: Comparison Of Ann and Anfis Modelssupporting

confidence: 92%

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

Taşan

2022

Appl Water Sci

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“…For future research direction, the data, models and input parameters uncertainties could be further analyzed and discussed [ 73 , 74 ]. Finally, global comparison of both the adopted scenarios revealed that, although considerable differences were not observed between the scenarios, the second scenario could provide promising outcomes in simulating groundwater quality parameters.…”

Section: Discussionmentioning

confidence: 99%

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Shiri

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

et al. 2021

PLoS ONE

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Groundwater is one of the most important freshwater resources, especially in arid and semi-arid regions where the annual amounts of precipitation are small with frequent drought durations. Information on qualitative parameters of these valuable resources is very crucial as it might affect its applicability from agricultural, drinking, and industrial aspects. Although geo-statistics methods can provide insight about spatial distribution of quality factors, applications of advanced artificial intelligence (AI) models can contribute to produce more accurate results as robust alternative for such a complex geo-science problem. The present research investigates the capacity of several types of AI models for modeling four key water quality variables namely electrical conductivity (EC), sodium adsorption ratio (SAR), total dissolved solid (TDS) and Sulfate (SO4) using dataset obtained from 90 wells in Tabriz Plain, Iran; assessed by k-fold testing. Two different modeling scenarios were established to make simulations using other quality parameters and the geographical information. The obtained results confirmed the capabilities of the AI models for modeling the well groundwater quality variables. Among all the applied AI models, the developed hybrid support vector machine-firefly algorithm (SVM-FFA) model achieved the best predictability performance for both investigated scenarios. The introduced computer aid methodology provided a reliable technology for groundwater monitoring and assessment.

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“…In many fields, conducting large-scale sampling is impractical due to its high cost and limited resources. Consequently, there is a need for monitoring approaches that are more cost-effective and expedited [ 4 ]. Simulation models, with their predictive capabilities, often serve as the sole feasible means for analyzing input data and facilitating management decision-making [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain

Morovvati Zarajabad,

Hadi,

Nabizadeh Nodehi

et al. 2024

Heliyon

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Modeling groundwater quality by using hybrid intelligent and geostatistical methods

Cited by 21 publications

References 71 publications

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

Estimation of groundwater quality using an integration of water quality index, artificial intelligence methods and GIS: Case study, Central Mediterranean Region of Turkey

Development of artificial intelligence models for well groundwater quality simulation: Different modeling scenarios

Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain

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