Evaluation of ground water quality contaminants using linear regression and artificial neural network models

Charulatha, G.; Srinivasalu, S.; Maheswari, O. Uma; Venugopal, T.; Giridharan, L.

doi:10.1007/s12517-017-2867-6

Cited by 45 publications

(16 citation statements)

References 15 publications

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“…Therefore, the measurement of ammonia nitrogen, phosphate, and chloride content in water is very important. At present, the methods for the determination of the properties and contents of organic matter in water are ultraviolet visible spectroscopy [14], mobile mass spectrometry [15], linear regression and artificial neural network [16], the sensor method [17], the trace element tracing method [18,19] and the potentiometric titration method [20]. Among them, ultraviolet visible spectroscopy, mobile mass spectrometry, and the sensor method need professional analytical instruments that come with high measurement cost; the linear regression and artificial neural network method needs a large number of training data to ensure the accuracy of the measurement, and the measurement efficiency is low; the trace element tracing method and potentiometric titration method have low operability and need professional technicians to operate in water quality detection.…”

Section: Introductionmentioning

confidence: 99%

Chromaticity Measurement Based on the Image Method and Its Application in Water Quality Detection

Cao

Zhu

Zhao

et al. 2019

Water

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In order to measure the chromaticity of water and the content of dissolved matter more accurately, effectively, and cheaply, a chromaticity measurement system based on the image method was proposed and applied. The measurement system used the designed acquisition device and image processing software to obtain the Red-Green-Blue (RGB) values of the image and converted the color image from RGB color space to Hue-Saturation-Intensity (HSI) space to separate the chromaticity and brightness. According to the definition of chromaticity, the hue (H), saturation (S) values, and chromaticity of standard chromaticity solution images were fitted by a non-linear surface, and a three-dimensional chromaticity measurement model was established based on the H and S values of water images. For the measurement of a standard chromaticity solution, the proposed method has higher accuracy than spectrophotometry. For actual water sample measurements, there is no significant difference between the results of this method and the spectrophotometer method, which verified the validity of the method. In addition, the system was tried to measure the concentration of ammonia nitrogen, phosphate, and chloride in water with satisfactory results.

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Section: Introductionmentioning

confidence: 99%

Chromaticity Measurement Based on the Image Method and Its Application in Water Quality Detection

Cao

Zhu

Zhao

et al. 2019

Water

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“…Keller et al [30] concluded that regression models, such as ANN and SVM were very valuable in estimating five water quality parameters, including Chl-a on the river Elbe in Germany. Considering that the SVM and ANN achieved the best result for different water quality parameters in several studies [26,[28][29][30], it can be expected that these models will obtain satisfactory Chl-a estimation results in this study.…”

Section: Introductionmentioning

confidence: 71%

“…Machine learning (ML) algorithms have demonstrated to be more effective than traditional approaches in determining the water quality [26] as they are very well-suited for predicting nonlinear and complex functions. Previous studies have confirmed the superiority or comparability of ML over traditional approaches in modelling water quality parameters [27][28][29]. ML provides the advantage of performing regressions without the need for a greater knowledge of the water body or the water quality parameters investigated [30].…”

Section: Introductionmentioning

confidence: 91%

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

Jimeno‐Sáez

Senent‐Aparicio

Cecilia

et al. 2020

IJERPH

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The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R2CV (cross-validated coefficient of determination) for the best-fit models.

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“…According to the obtained results, the ANN structured with three hidden layers and 18 neurons was the best-performing model which results in cost reduction by about 150,000 dollars daily. Charulatha, Srinivasalu [21] employed different regression and ANN models for quality assessment of groundwater pollutants. The study was conducted to detect of nitrite ion concentration for the potential pollutants in the groundwater.…”

Section: Introductionmentioning

confidence: 99%

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

Azimi

Moghaddam

Monfared

2018

J Environ Health Sci Engineer

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Background The level of groundwater resources is changing rapidly and this requires the discovery of newer groundwater resources. Drought is one of the most significant natural phenomena affecting different aspects of human life and environment. During the last decades, the application of artificial intelligent techniques has been recognized as effective approaches to forecast an annual precipitation rate. Method In this study, the association analysis of climate drought and a decline in groundwater level is addressed using Gaussian process classification (GPC) and backpropagation (BP) artificial neural network (ANN). This methodology is proposed to create a framework for decision making and reduce uncertainty in water resource management calculations, and in particular to optimize the management of groundwater drinking water sources. Results Underground water levels in 609 study plains in Iran were used to predict drought over the test period, extending from 2017 to 2021. The artificial intelligence methods were implemented in the Python programming environment to achieve an annual precipitation rate. A statistical summary of the Rasterized Cells of the zoning maps was used to validate the prediction results. Considering the relationship between water quality reductions and drought in Iranian aquifers due to the occurrence of groundwater drought periods, the results were validated by analysis of the effect of climate drought using the Standardized Precipitation Index (SPI) on the occurrence of observed droughts with the Groundwater Resources Index (GRI). The results are well-illustrated by the observation of the predicted digits in the third dimension of the Gaussian distribution. Conclusion According to the SPI indicator, the southern regions of the country, and especially the central parts of the plain, can be considered the most affected areas by the most severe future droughts. The prediction results indicate a decrease in drought severity as part of a two-year sequence involving a recurrence of drought exacerbation and relative decline, as well as a failed state after the critical condition of aquifers.

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Evaluation of ground water quality contaminants using linear regression and artificial neural network models

Cited by 45 publications

References 15 publications

Chromaticity Measurement Based on the Image Method and Its Application in Water Quality Detection

Chromaticity Measurement Based on the Image Method and Its Application in Water Quality Detection

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

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