Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods

Eskandari, Effat; Mohammadzadeh, Hossein; Nassery, Hamid Reza; Vadiati, Meysam; Zadeh, Alireza Mohammad; Kişi, Özgür

doi:10.1016/j.jhydrol.2022.127706

Cited by 26 publications

(12 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…K-mean clustering has been utilized by researchers in GW quality studies [ 40 , 54 ]. It is a useful tool for defining hydrochemical processes that control water quality for water quality assessment [ 55 ]. In this study, the K-mean clustering algorithm was applied to the entire GW dataset to group the water samples into different clusters.…”

Section: Resultsmentioning

confidence: 99%

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

2023

View full text Add to dashboard Cite

The degradation of groundwater (GW) quality due to seawater intrusion (SWI) is a major water security issue in water-scarce regions. This study aims to delineate the impact of SWI on the GW quality of a multilayered aquifer system in the eastern coastal region of Saudi Arabia. The physical and chemical properties of the GW were determined via field investigations and laboratory analyses. Irrigation indices (electrical conductivity (EC), potential salinity (PS), sodium adsorption ratio (SAR), Na%, Kelly’s ratio (KR), magnesium adsorption ratio (MAR), and permeability index (PI)) and a SWI index (fsea) were obtained to assess the suitability of GW for irrigation. K-mean clustering, correlation analysis, and principal component analysis (PCA) were used to determine the relationship between irrigation hazard indices and the degree of SWI. The tested GW samples were grouped into four clusters (C1, C2, C3, and C4), with average SWI degrees of 15%, 8%, 5%, and 2%, respectively. The results showed that the tested GW was unsuitable for irrigation due to salinity hazards. However, a noticeable increase in sodium and magnesium hazards was also observed. Moreover, increasing the degree of SWI (fsea) was associated with increasing salinity, sodium, and magnesium, with higher values observed in the GW samples in cluster C1, followed by clusters C2, C3, and C4. The correlation analysis and PCA results illustrated that the irrigation indices, including EC, PS, SAR, and MAR, were grouped with the SWI index (fsea), indicating the possibility of using them as primary irrigation indices to reflect the impact of SWI on GW quality in coastal regions. The results of this study will help guide decision-makers toward proper management practices for SWI mitigation and enhancing GW quality for irrigation.

show abstract

Section: Resultsmentioning

confidence: 99%

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

2023

View full text Add to dashboard Cite

show abstract

“…The Gibbs diagram is used to understand the processes that control the ion formation in groundwater can reveal the origin of salts in groundwater as well as the chemistry of ion formation and the identification of the main source of increasing salinity. Interpretations of the hydrochemical facies are useful tools for determining the chemical history of groundwater bodies and for distinguishing between different types of groundwater depending on the presence of dominant ions [31]. Due to the hydrochemical classification, mixtures of end-element compositions can be identified using the rhombic field assuming that all species initially identified in the two mixing waters remain in solution during mixing.…”

Section: Piper and Gibbs Diagrammentioning

confidence: 99%

Groundwater Quality Affected by the Pyrite Ash Waste and Fertilizers in Valea Calugareasca, Romania

Vasilache

Diacu

Modrogan

et al. 2022

Water

View full text Add to dashboard Cite

The aim of the study was to assess the groundwater quality in a rural area affected by the abandoned pyrite ash waste dumps. The abundance of major ions in groundwater depends largely on the nature of the rocks, climatic conditions, and mobility. To evaluate geochemical processes, 30 groundwater samples collected from Valea Calugareasca, Prahova County, Romania, were analyzed for the major anions (NO3−, SO42−, Cl−, HCO3−, and F−) and cations (Ca2+, Mg2+, Na+, and K+), which are naturally highly variable due to climatic and geographical location conditions. Ca2+, Na+, Mg2+, and K+ varied between 118 and 275 mg/L, 32 and 160 mg/L, 12.2 and 78.4 mg/L, and 0.21 and 4.48 mg/L, respectively. NO3− levels exceeding the World Health Organization (WHO) limit of 50 mg/L were identified in 17% of the groundwater samples, mainly as result of fertilizers applied to agricultural activities. The hydrogeochemical study identified dolomite dissolution and halite precipitation as natural sources of ions as well as the presence of pyrite as a source of SO42− ions in 60% of the samples. The sulfate content varied between 125 and 262 mg/L. Bicarbonate and chloride concentrations varied between 202 and 530 mg/L and 21 and 212 mg/L. The saturation index indicates the contribution of Ca2+ ions in the groundwater samples came from some processes of dissolving rocks such as aragonites (values between 1.27 and 2.69) and calcites (values between 1.43 and 2.82). Negative halite values indicated that salt accumulation results from precipitation processes. Only 10% of the analyzed groundwater samples were suitable for human consumption, the samples being situated on the hill, far away from the pyrite ash waste dumps and agricultural land.

show abstract

“…researchers such as Yanrui Ning, Mohammed Achite, MS Mehmood, and SJ Parreño [2][3][4][5] have utilized ARIMA models to conduct real-time quantitative predictions on various research topics, capturing future trends in these subjects. Additionally, researchers like E Eskandari, Bo Li, Sangeeta Sangeeta, and Dini Rohmayani [6][7][8][9] have employed prediction or classification techniques on different research subjects and have drawn experimental conclusions by comparing classification results. However, the models proposed by these researchers have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Word game prediction and difficulty classification based on machine learning

Zhang

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

View full text Add to dashboard Cite

This article focuses on the Wordle game, a word puzzle game that has become extremely popular on social media. Based on the research objectives, three models have been proposed to solve the problem: a model for predicting the number of players, a model for predicting the number of attempts, and a model for classifying word difficulty. For the problem of predicting the number of players, the (Autoregressive Integrated Moving Average-Support Vector Machine) ARIMA-SVM model was proposed. The results showed that the ARIMA-SVM model performed about 17% better than the ARIMA model. To address the problem of predicting the number of attempts, the Random Forest Regression model was proposed. In regard to the problem of classifying word difficulty, the GA-FCM clustering model was proposed. Finally, this paper contributes to the difficulty classification of words based on different word attributes, and the experimental results showed that (Genetic Algorithm-Fuzzy C-Means) GA-FCM performed significantly better than the FCM model in all indicators.

show abstract

Delineation of isotopic and hydrochemical evolution of karstic aquifers with different cluster-based (HCA, KM, FCM and GKM) methods

Cited by 26 publications

References 55 publications

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

Effects of Seawater Intrusion on the Groundwater Quality of Multi-Layered Aquifers in Eastern Saudi Arabia

Groundwater Quality Affected by the Pyrite Ash Waste and Fertilizers in Valea Calugareasca, Romania

Word game prediction and difficulty classification based on machine learning

Contact Info

Product

Resources

About