Hydrogeochemical investigation of geothermal springs in Erzurum, East Anatolia (Turkey)

Alacali, Mine

doi:10.1007/s12665-018-7986-1

Cited by 7 publications

(2 citation statements)

References 25 publications

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“…This parameter has a direct relationship with hardness, which is usually mainly due to the presence of calcium and magnesium expressed as CaCO 3 . Other ions, such as iron or manganese, also contribute to this parameter [16,17]. The hardness value depends on pH and alkalinity values since the pH determines the dissolution capacity of the minerals in the water.…”

Section: Physicochemical Parametersmentioning

confidence: 99%

Hydrogeochemical Characteristics and Assessment of Drinking Water Quality in the Urban Area of Zamora, Mexico

Reyes-Toscano

Alfaro-Cuevas-Villanueva

Cortés-Martínez

et al. 2020

Water

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This work assessed the groundwater hydrogeochemistry and the drinking water quality of 10 wells supplying the urban area of Zamora, Michoacán, Mexico. Two sampling campaigns were conducted in May 2018 (dry season) and November 2018 (wet season) to describe the chemistry of the water and its interaction with the rock. Physical and chemical constituents (temperature, pH, electrical conductivity, color, turbidity, solids, total hardness, total alkalinity, chemical and biochemical oxygen demands), major components (Ca2+, Mg2+, Na+, K+, SO42−, PO43−, HCO3−, CO32−, Cl−, N-NO3−, and N-NH3), as well as trace elements (As, Fe, Mn, Ba, Al, Sb, Co, V, Cu, Cd, Cr, Ni, Zn, Tl, Pb) were analyzed. Results showed groundwater with a slight tendency to alkalinity. The hydrogeochemical facies observed are Ca2+-HCO3− in all sites. Hydrochemical diagrams indicate immature, cold, non-saline, and uncontaminated water with short residence time. Water–rock interaction predominates. The water in the study area is appropriate for drinking use according to Mexican and international regulations with an excellent quality in 7 wells and good in the other 3.

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Section: Physicochemical Parametersmentioning

confidence: 99%

Hydrogeochemical Characteristics and Assessment of Drinking Water Quality in the Urban Area of Zamora, Mexico

Reyes-Toscano

Alfaro-Cuevas-Villanueva

Cortés-Martínez

et al. 2020

Water

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“…Extensive research is vital prior to drilling in geothermal resource areas to determine the most suitable locations, which can help reduce the costs associated with geothermal drilling and enhance the utilization of geothermal resources. Identifying RTs of geothermal waters necessitates complex geological surveys and analyses, which are time-consuming, costly, and complex [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

Gurgenc,

Altay,

Altay

2024

Applied Sciences

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To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

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Characterization of discharge, hydrogeochemical process and evaluation of water quality of some warm and cold springs, northeastern and southeastern Nigeria

Edet,

Akpan,

Yakubu

et al. 2024

J Earth Syst Sci

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Hydrogeochemical investigation of geothermal springs in Erzurum, East Anatolia (Turkey)

Cited by 7 publications

References 25 publications

Hydrogeochemical Characteristics and Assessment of Drinking Water Quality in the Urban Area of Zamora, Mexico

Hydrogeochemical Characteristics and Assessment of Drinking Water Quality in the Urban Area of Zamora, Mexico

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

Characterization of discharge, hydrogeochemical process and evaluation of water quality of some warm and cold springs, northeastern and southeastern Nigeria

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