Influence of variations in rainfall pattern on the hydrogeochemistry of coastal groundwater—an outcome of periodic observation

Thilagavathi, R.; Chidambaram, S.; Thivya, C.; Keesari, Tirumalesh; Senapathi, Venkatramanan; Pethaperumal, S.; Prasanna, Mohan Viswanathan; Nagappan, Ganesh

doi:10.1007/s11356-019-05962-w

Cited by 23 publications

(6 citation statements)

References 61 publications

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“…The correlation between the year and parameters such as EC suggests a worsening trend in water quality over time, mainly due to increased residential density and insufficient wastewater management practices [36]. Similar to Thuckalay, strong negative correlations with pH and positive correlations with EC and various ions suggest the impact of urban runoff and domestic wastewater on water quality in Villukuri [37,38]. The consistent pattern across residential areas highlights the need for better wastewater treatment and urban planning to mitigate these impacts.…”

Section: Resultsmentioning

confidence: 97%

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Ramesh,

Vanitha

2024

Water

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The assessment of water quality assumes a position of utmost significance as it plays a critical role in upholding ecological balance and safeguarding the well-being of human populations. To achieve these goals, an in-depth consideration of water quality trends is essential, as it offers comprehension into the intricate interplay between various elements within aquatic ecosystems. As a consequence, the proposed work investigates the water quality trends specifically within the Valliyar sub-basin, which encompasses the geographical areas of Kattathurai, Colachal, Thuckalay, and Villukuri. The temporal scope of investigation spans from the year 2000 to 2018 using the dependent variable of water quality parameters with dependent variables of climate data. Recognizing the need for advanced methodologies to tackle the multifaceted nature of water quality dynamics, this research harnesses the power of pioneering machine learning techniques. Two notable approaches, the Radial Bias Function Neural Network (RBFNN) and the DenseNet-121-based Convolutional Neural Network (CNN), are brought into performance. The primary objective is to leverage these techniques to forecast water quality trends for the next twenty-two years. The effectiveness of various machine learning models in predicting water quality is evaluated using the following key performance metrics: the Mean-Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE). Notably, the DenseNet CNN model exhibits accurate prediction among the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning (DL) models. This research underscores the significance of machine learning techniques, with DenseNet CNN model emerging as a particularly promising tool in this domain.

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Section: Resultsmentioning

confidence: 97%

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Ramesh,

Vanitha

2024

Water

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“…Hydrogeochemical simulation is an important method to analyze various geochemical interactions in the water-rock interaction system on the basis of equilibrium thermodynamics and chemical kinetics [40,41]. To further analyze the evolution law of groundwater in the study area, two representative groundwater runoff paths were selected, and PHREEQC software (USGS, Virginia State, USA) was used to perform a reverse hydrogeochemical simulation to quantitatively explain the changes of groundwater chemical composition and the main water-rock interactions.…”

Section: Hydrogeochemical Modelingmentioning

confidence: 99%

Characteristics and Controlling Factors of Groundwater Hydrochemistry in Dongzhi Tableland Area of the Loess Plateau of Eastern Gansu—A Case Study of Ning County Area, North China

Zhang

Han

Wang

et al. 2022

Water

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Groundwater plays an irreplaceable role in all aspects of the Loess Plateau. In this study, the loess phreatic water (LPW) and bedrock phreatic water (BPW) in the Ning County area (NCA) were sampled and analyzed, and the characteristics and controlling factors of groundwater were determined by using statistical analysis, hydrochemical methods, and hydrogeochemical simulation. The results indicated that the groundwater in the NCA was alkaline as a whole, and the average pH values of LPW and BPW were 8.1 and 7.8, respectively. The mean values of TDS concentrations of LPW and BPW were 314.9 mg/L and 675.3 mg/L, and the mean values of TH contents were 194.6 mg/L and 286.6 mg/L, respectively, which were mainly divided into hard fresh water. The Piper diagram illustrated that the hydrochemical type of groundwater in the NCA was mainly the HCO3·Ca type. The main recharge source of groundwater was atmospheric precipitation, and it was affected by evaporation to a certain extent. The linear relationships of δ18O and δ2H of LPW and BPW were δ2H = 6.998δ18O − 3.802 (R2 = 0.98) and δ2H = 6.283δ18O − 10.536 (R2 = 0.96), respectively. Hydrochemical analysis indicated that the groundwater in the NCA was mainly controlled by rock weathering and cation exchange. BPW was affected by the dissolution of gypsum. The possible mineral phases were identified on the basis of the main soluble minerals in the aquifer, and hydrogeochemical reverse simulations were performed. The dissolution of calcite, illite, and hornblende, and the precipitation of dolomite, plagioclase, and microcline occurred on both the LPW and BPW pathways.

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“…Similarly, in factor IV, there is a positive loading of Ca and Mg with very low loading of Na and K, which clearly implies the base ion exchange process. The factor I with positive loading of Mg, Na, Cl, TDS and EC (Table 7) is linked to percolation of secondary salts during SWM [60,71] deposited in the pore spaces during previous summer. The factor II, with a positive loading of K and PO 4 , indicates the influence of anthropogenic sources mainly from agricultural activities [93].…”

Section: Factor Analysismentioning

confidence: 99%

“…The studies on geochemical processes for limited area and limited samples were attempted [48,49] and inferred that the geochemical weathering and ion exchange are the predominant factors. Apart from these studies, geophysical studies using borehole data, to determine the lithological variations [48]; studies on the geomorphology of the region [50]; saturation index of groundwater [51]; assessment of rainfall recharge using stable isotopes [52]; microbial contamination [53]; budgeting of groundwater with respect to water level variations [54] and dissolved organic carbon in groundwater [55][56][57][58][59][60] were also attempted by earlier researchers. The current study integrates the geophysical techniques such as resistivity and lithology, the temporal change of water level and rainfall along with the hydrogeochemical investigation of groundwater samples to assess the salinity in this coastal aquifer.…”

Section: Introductionmentioning

confidence: 99%

Geochemical variations due to salinization in groundwater along the southeast coast of India

et al. 2021

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In recent years, the extraction of groundwater (GW) in coastal aquifers has rendered the fragile aquifers more saline due to the sea water intrusion. Groundwater from the coastal aquifers of the Pudhucherry region were sampled to study the process of salinization. An integrated approach was adopted to identify the salinization process, by coordinating the results of borehole geophysics, rainfall pattern, water level variation, hydrochemical characters and multivariate statistical analysis. A total of 136 groundwater samples were collected during two different seasons, southwest monsoon (SWM) and northeast monsoon (NEM). The major cations and anions were analyzed adopting standard procedures. Resistivity and litholog indicate that the southeastern (SE) part of the study region has lower resistivity than in north. Based on electrical conductivity (EC) and total dissolved solids (TDS) values, most of the samples are potable, except for few samples from southeastern region. The study results indicated that higher values of Na, EC, K, SO4, Mg and Cl were observed during NEM, indicating leaching of salt into the aquifer and ion exchange process. The predominant hydrochemical facies of groundwater was Na-Cl and Ca–Mg–Cl type reflecting the saline water and the mixing process of saltwater and fresh groundwater, respectively. Though more number of samples with higher EC was noted in NEM, the results of PCA and correlation analysis indicate the predominance of leaching of salts and intense agricultural activities. The process of sea water intrusion was observed to be dominant during SWM.

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Influence of variations in rainfall pattern on the hydrogeochemistry of coastal groundwater—an outcome of periodic observation

Cited by 23 publications

References 61 publications

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Characteristics and Controlling Factors of Groundwater Hydrochemistry in Dongzhi Tableland Area of the Loess Plateau of Eastern Gansu—A Case Study of Ning County Area, North China

Geochemical variations due to salinization in groundwater along the southeast coast of India

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