Spatiotemporal mapping of groundwater salinity in Al-Batinah, Oman

Abulibdeh, Ammar; Al‐Awadhi, Talal; Nasiri, Noura Al; Al-Buloshi, Ali; Abdelghani, Montasser

doi:10.1016/j.gsd.2021.100551

Cited by 25 publications

(14 citation statements)

References 59 publications

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“…They found that the factors precipitation, altitude, groundwater depth, and distance from the residential area are the most important factors for predicting nitrate pollution. Further, many studies have shown a high performance of combining artificial intelligence and GIS capabilities in groundwater quality modeling (Tweed et al, 2007;Arslan, 2012;Haselbeck et al, 2019;Abulibdeh et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran

Gholami

Booij

2022

Journal of Cleaner Production

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Section: Introductionmentioning

confidence: 99%

Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran

Gholami

Booij

2022

Journal of Cleaner Production

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“…Intensification of this trend can be considered a serious threat to human life. Therefore, it is necessary to plan for managing groundwater resources and the risk of dropping groundwater depth (Abulibdeh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

“…The GIS can be used as an efficient tool in the model running and mapping in groundwater studies. Past studies indicate the high performance of combining artificial intelligence and GIS capabilities in modeling the quantity and quality of groundwater (Ganapuram et al 2009;Bradai et al 2016;Delsman et al 2018;Haselbeck et al 2019;Abulibdeh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatial modeling of groundwater depth fluctuations using co-active neuro-fuzzy inference system (CANFIS) and geographic information system (GIS)

Gholami

2022

Appl Water Sci

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Groundwater drawdown and reduction of groundwater resources are serious problems in the water supply. Unfortunately, sufficient and accurate data on the groundwater depth fluctuations values are not available in all plains of the world. In the present study, a co-active neuro-fuzzy inference system (CANFIS) network and geographic information system (GIS) were used to simulate the annual groundwater drawdown values on the southern coasts of the Caspian Sea (Iran). For this purpose, the annual drawdown values in 250 piezometric wells as the output variable were evaluated. Further, the effective factors in groundwater depth fluctuations were evaluated as inputs of the CANFIS network, including water table depth, annual precipitation, annual evaporation, the transmissivity of aquifer formations, topography, distance from the sea, distance from water sources, population density, and withdrawal values in the influence radius of the study wells. The modeling process was performed in three stages: training, cross-validation, and test. According to the results, the CANFIS network has a high performance in modeling groundwater drawdown (training R-sqr = 0.97; test R-sqr = 0.82). Then, the inputs of the tested network were prepared in the GIS in raster format for the study plain. The tested network was used to simulate the annual fluctuations of groundwater depth in the plain and the simulation results were presented in the GIS as an annual groundwater drawdown map. Overlap and comparison of the observed values of groundwater drawdown and the simulated values in the map indicate the high performance of the CANFIS network (R-sqr = 0.81). Therefore, the used methodology can be applied to simulate the annual drawdown of groundwater depth in plains without groundwater depth data.

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“…Unfortunately, the increase in human population and agricultural and industrial developments have led to an increase in groundwater withdrawal, pollution, and depletion of these resources (Shivasorupy et al 2012). Moreover, climate change and alteration of precipitation patterns have exerted extra pressure on groundwater resources in these regions making aquifers depletions a serious threat to human life (Abulibdeh et al 2021). Therefore, access to groundwater uctuations data and aquifer drawdown is essential for water resources management plans.…”

Section: Introductionmentioning

confidence: 99%

“…Combining AI-based models with GIS capabilities is a common exercize in groundwater modeling. Previous studies have shown the high performance of this exercize in modeling the quantity and quality of groundwater resources (Tweed et al 2007;Ganapuram et al 2009;Arslan 2012;Bradai et al 2016;Haselbeck et al 2019;Sahour et al 2020;Abulibdeh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Prediction of groundwater drawdown using artificial neural networks

Gholami

Sahour

2022

Environ Sci Pollut Res

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Groundwater drawdown is typically measured using pumping tests and eld experiments; however, the traditional methods are time-consuming and costly when applied to extensive areas. In this research, a methodology is introduced based on arti cial neural network (ANN)s and eld measurements in an alluvial aquifer in the north of Iran. First, the annual drawdown as the output of the ANN models in 250 piezometric wells was measured, and the data were divided into three categories of training data, cross-validation data, and test data. Then, the effective factors in groundwater drawdown including groundwater depth, annual precipitation, annual evaporation, the transmissivity of the aquifer formation, elevation, distance from the sea, distance from water sources (recharge), population density, and groundwater extraction in the in uence radius of each well (1000 m) were identi ed and used as the inputs of the ANN models. Several ANN methods were evaluated, and the predictions were compared with the observations. Results show that, the modular neural network (MNN) showed the highest performance in modeling groundwater drawdown (Training R-sqr = 0.96, test R-sqr = 0.81). The optimum network was tted to available input data to map the annual drawdown across the entire aquifer. The accuracy assessment of the nal map yielded favorable results (R-sqr = 0.8). The adopted methodology can be applied for the prediction of groundwater drawdown in the study site and similar settings elsewhere.

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Spatiotemporal mapping of groundwater salinity in Al-Batinah, Oman

Cited by 25 publications

References 59 publications

Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran

Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran

Spatial modeling of groundwater depth fluctuations using co-active neuro-fuzzy inference system (CANFIS) and geographic information system (GIS)

Prediction of groundwater drawdown using artificial neural networks

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