Development of a water quality index (WQI) for the Loktak Lake in India

Kangabam, Rajiv Das; Bhoominathan, Sarojini Devi; Kanagaraj, Suganthi; Govindaraju, M.

doi:10.1007/s13201-017-0579-4

Cited by 165 publications

(54 citation statements)

References 45 publications

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“…To measure water quality, WQI is used to be calculated using various parameters that significantly affect WQ [ 40 – 42 ]. In this study, a published dataset is considered to test the proposed model, and seven significant water quality parameters are included.…”

Section: Methodsmentioning

confidence: 99%

Water Quality Prediction Using Artificial Intelligence Algorithms

Aldhyani

Al-Yaari

Alkahtani

et al. 2020

Applied Bionics and Biomechanics

229

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During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K -nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient ( RNARNET = 96.17 % and RLSTM = 94.21 % ). This kind of promising research can contribute significantly to water management.

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Section: Methodsmentioning

confidence: 99%

Water Quality Prediction Using Artificial Intelligence Algorithms

Aldhyani

Al-Yaari

Alkahtani

et al. 2020

Applied Bionics and Biomechanics

229

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“…We had five parameters, namely fecal coliform, pH, temperature, turbidity and total dissolved solids in our dataset. We also considered nitrites as the sixth parameter as the weight and relative importance of nitrites in the WQI calculation is stated to be equal to that of nitrates in multiple WQI studies [13][14][15]. Using these parameters and their assigned weightages, we calculated the WQI of each sample as reflected in Equation (1), where q value reflects the value of a parameter in the range of 0-100 and w_ f actor represents the weight of a particular parameter as listed in Table 2.…”

Section: Water Qualiity Index (Wqi)mentioning

confidence: 99%

Efficient Water Quality Prediction Using Supervised Machine Learning

Ahmed

Mumtaz

Anwar

et al. 2019

Water

297

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Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.

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“…WQI aims to understand the overall state of the water quality and has been applied to both the surface water and groundwater quality evaluation around the world since the past few decades (Sanjib Kumar and Chakrabarty 2007;Khwakaram et al 2012;Ravikumar et al 2013;Bhutiani et al 2014;Kirubakaran et al 2015;Yaseen et al 2015;Puri et al 2015;Krishan et al 2016;Bora and Goswami 2017;Shah and Joshi 2017;Wagh et al 2017;Kangabam et al 2017). The main object of WQI development is to convert a complex set of water quality data into clear and useful information which enables decision-makers to make the decision easily about the state of the water source (Akoteyon et al 2011;Balan et al 2012).…”

Section: Introductionmentioning

confidence: 99%