Probability mapping of groundwater contamination by hydrocarbon from the deep oil reservoirs using GIS-based machine-learning algorithms: a case study of the Dammam aquifer (middle of Iraq)

Al-Mayahi, Huda M.; Al-Abadi, Alaa M.; Fryar, Alan E.

doi:10.1007/s11356-020-11158-4

Cited by 7 publications

(2 citation statements)

References 38 publications

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“…The hybrid MLP-RS model achieved high validation scores and indicated that slope, elevation, TRI and HAND are the most important predictors of groundwater presence. A novel method was suggested in another study (Al-Mayahi, Al-Abadi, & Fryar, 2021) for the spatial delineation of groundwater contamination in aquifers specifically focusing on the Dammam Formation in the southern and western deserts of Iraq. Three machine learning classifiers; backpropagation multi-layer perceptron artificial neural networks (ANN), support vector machine with radial basis function (SVM-radial), and random forest (RF) with GIS, were used to map the probability of contamination in this aquifer.…”

Section: Applications In Groundwater Detection and Contaminationmentioning

confidence: 99%

Merging GIS and Machine Learning Techniques: A Paper Review

Ekeanyanwu¹,

Obisakin²,

Aduwenye³

et al. 2022

GEP

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GIS (Geographic Information Systems) data showcase locations of earth observations or features, their associated attributes and spatial relationships that exist between such observations. Analysis of GIS data varies widely and may include some modeling and predictions which are usually computing-intensive and complicated, especially, when large datasets are involved. With advancement in computing technologies, techniques such as Machine learning (ML) are being suggested as a potential game changer in the analysis of GIS data because of their comparative speed, accuracy, automation, and repeatability. Perhaps, the greatest benefit of using both GIS and ML is the ability to transfer results from one database to another. GIS and ML tools have been used extensively in medicine, urban development, and environmental modeling such as landslide susceptibility prediction (LSP). There is also the problem of data loss during conversion between GIS systems in medicine, while in geotechnical areas such as erosion and flood prediction, lack of data and variability in soil has limited the use of GIS and ML techniques. This paper gives an overview of the current ML methods that have been incorporated into the spatial analysis of data obtained from GIS tools for LSP, health, and urban development. The use of Supervised Machine Learning (SML) algorithms such as decision trees, SVM, KNN, and perceptron including Unsupervised Machine Learning algorithms such as k-means, elbow algorithms, and hierarchal algorithm have been discussed. Their benefits, as well as their shortcomings as studied by several researchers have been elucidated in this review. Finally, this review also discusses future optimization techniques.

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Section: Applications In Groundwater Detection and Contaminationmentioning

confidence: 99%

Merging GIS and Machine Learning Techniques: A Paper Review

Ekeanyanwu¹,

Obisakin²,

Aduwenye³

et al. 2022

GEP

View full text Add to dashboard Cite

show abstract

“…For the past few years, machine learning has been applied as an efficient tool for pollution identification, risk assessment, and migration prediction in subsurface environments. ,− Machine learning is a mathematical prediction model based on investigating the correlation between independent and dependent variables . Some algorithms including support vector machine (SVM), random forest (RF), artificial neural network (ANN), decision tree, principal component analysis, and extreme gradient boosting are commonly used in environmental assessment. , Nafouanti et al collected the chemical factors of 482 groundwater samples and evaluated the performance of three statistical technologies, which showed that RF outperformed ANN and logistic regression in predicting groundwater fluoride contamination and eight water chemistry indicators were screened, which attributed to the groundwater fluoride .…”

Section: Introductionmentioning

confidence: 99%