Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)

Alagha, Jawad S.; Seyam, Mohammed; Said, Azlin Md; Mogheir, Yunes

doi:10.1007/s10040-017-1658-1

Cited by 24 publications

(11 citation statements)

References 67 publications

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“…The use of machine learning methods also has been limited in a few modeling experiments even though they appeared more convenient and performed with higher accuracy, better generalization ability, more reliability, and efficiency compared to the statistical, numerical, and physical models. For instance, Alagha et al [82] used artificial neural networks (ANNs) and support vector machine (SVM) to model salinization processes with higher performance and more simplicity compared to statistical methods. Yu et al [53] also reported machine learning models of back propagation ANN (BP-ANN) and neuro-fuzzy (NF) superior to the conventional linear models in the prediction of groundwater salinity hazards.…”

Section: Introductionmentioning

confidence: 99%

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

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Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (> 0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.

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Section: Introductionmentioning

confidence: 99%

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

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“…They pointed out that the limited data set was one of the drawbacks of their research and encouraged others to collect more data to recalibrate and revalidate the model. Wang et al [19] employed a typical three-layer of MLP structure [77][78][79][80][81][82][83][84][85][86][87][88][89] with the BP algorithm to achieve Chl-a prediction. They divided the dataset into training (75%) and testing parts (25%).…”

Section: Artificial Neural Network Models For Water Quality Predictionmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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“…Finally, two studies have employed complex reactive transport models to quantify biogeochemical reactions induced by SWI [157][158][159]. Besides the physically based density dependent numerical models that need a large number of field data to be calibrated, there has recently been a growing number of papers that have employed the surrogate models, such as non-linear regressive techniques, to describe the SWI phenomenon [160][161][162][163][164], or using simple spreadsheet macros to plot the hydrochemical facies evolution [165]. In general, numerical models have been widely used to quantify the different origins of salinization via density dependent flow and transport models [166][167][168][169][170][171][172][173][174][175].…”

Section: Data Handling Techniquesmentioning

confidence: 99%

The Issue of Groundwater Salinization in Coastal Areas of the Mediterranean Region: A Review

Mastrocicco

Colombani

2021

Water

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The Mediterranean area is undergoing intensive demographic, social, cultural, economic, and environmental changes. This generates multiple environmental pressures such as increased demand for water resources, generation of pollution related to wastewater discharge, and land consumption. In the Mediterranean area, recent climate change studies forecast large impacts on the hydrologic cycle. Thus, in the next years, surface and ground-water resources will be gradually more stressed, especially in coastal areas. In this review paper, the historical and geographical distribution of peer-review studies and the main mechanisms that promote aquifer salinization in the Mediterranean area are critically discussed, providing the state of the art on topics such as actual saltwater wedge characterization, paleo-salinities in coastal areas, water-rock interactions, geophysical techniques aimed at delineating the areal and vertical extent of saltwater intrusion, management of groundwater overexploitation using numerical models and GIS mapping techniques for aquifer vulnerability to salinization. Each of the above-mentioned approaches has potential advantages and drawbacks; thus, the best tactic to tackle coastal aquifer management is to employ a combination of approaches. Finally, the number of studies focusing on predictions of climate change effects on coastal aquifers are growing but are still very limited and surely need further research.

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Integrating an artificial intelligence approach with k-means clustering to model groundwater salinity: the case of Gaza coastal aquifer (Palestine)

Cited by 24 publications

References 67 publications

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

A Review of the Artificial Neural Network Models for Water Quality Prediction

The Issue of Groundwater Salinization in Coastal Areas of the Mediterranean Region: A Review

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