Artificial intelligence for the prediction of water quality index in groundwater systems

Sakizadeh, Mohamad

doi:10.1007/s40808-015-0063-9

Cited by 95 publications

(33 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These studies used more than 10 water quality parameters to predict WQI. Ahmad et al [8] used 25 input parameters, Sakizadeh [9] used 16 parameters, Gazzaz et al [4] used 23 input parameters in their methodology, and Rankovic et al [12] used 10 input parameters, which is unsuitable for inexpensive real time systems. Whereas, our methodology employs only four water quality parameters to predict WQI, with a MAE of 1.96, and to predict water quality class with an accuracy of 85%.…”

Section: Discussionmentioning

confidence: 99%

“…The use of 25 parameters makes their solution a little immoderate in terms of an inexpensive real time system, given the price of the parameter sensors. Sakizadeh [9] predicted the WQI using 16 water quality parameters and ANN with Bayesian regularization. His study yielded correlation coefficients between the observed and predicted values of 0.94 and 0.77, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Water Quality Prediction Using Supervised Machine Learning

Ahmed

Mumtaz

Anwar

et al. 2019

Water

280

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Efficient Water Quality Prediction Using Supervised Machine Learning

Ahmed

Mumtaz

Anwar

et al. 2019

Water

280

View full text Add to dashboard Cite

show abstract

“…Some different applications are as follows. Prediction of uniaxial compressive strength of travertine rocks ; temperature variations and generate missing temperature data in Iran (Salami and Ehteshami 2016); river flow forecasting (Kasiviswanathan and Sudheer 2016); prediction of water quality index in groundwater systems (Sakizadeh 2016 (Javan et al 2015); runoff and sediment yield modeling (Sharma et al 2015); modeling Secchi disk depth (SD) in river (Heddam 2016b); and predicting phycocyanin (PC) pigment concentration in river (Heddam 2016c).…”

Section: Multilayer Perceptron Neural Network (Mlpnn)mentioning

confidence: 99%

Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA

Heddam

2016

Model. Earth Syst. Environ.

View full text Add to dashboard Cite

In the present study, we developed and compared two artificial intelligences technique (AI) for simultaneous modelling and forecasting hourly dissolved oxygen (DO) in river ecosystem. The two techniques are: radial basis function neural network (RBFNN) and multilayer perceptron neural network (MLPNN). For the purpose of the study, we choose two stations from the United States Geological Survey: (USGS ID: 421015121471800) at Lost River Diversion Channel nr Klamath River, Oregon, USA (Latitude 42°10 0 15 00 , Longitude 121°47 0 18 00 NAD83), with a total of 8703 data, and (USGS ID: 421401121480900) at Upper Klamath Lake at Link River Dam, Oregon USA (Latitude 42°14 0 01 00 , Longitude 121°48 0 09 00 NAD83) with a total of 8552 data. The investigation is divided into two distinguished phase. Firstly, using four water quality variables that are, water pH, temperature (TE), specific conductance (SC), and sensor depth (SD); we compared five models (M1 to M5) with different combination of input variables. As a result of the first investigation we found that generally RBFNN outperform MLPNN according to the performances criteria calculated. In the second part of the study, six Different models (FM1 to FM6) having the same input data sets are developed for 1,12, 24,48,72 and 168 h ahead (in advance) forecasting. The performance of the RBFNN and MLPNN models in training, validation and testing sets are compared with the observed data. Our results reveal that the two models provided relatively similar results and they successfully forecasting DO with a high level of accuracy and the reliability of forecasting decreases with increasing the step ahead.

show abstract

“…This method is used for modifying some of the objective functions such as mean square error (MSE) with aim to improve the model's generalization capability. It was seen that performance of Bayesian regularization method was better than the other two methods in predicting WQI followed by ensemble and early stopping (Sakizadeh 2016).…”

Section: Introductionmentioning

confidence: 99%

Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

et al. 2019

View full text Add to dashboard Cite

In this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoTbased water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

show abstract

Artificial intelligence for the prediction of water quality index in groundwater systems

Cited by 95 publications

References 22 publications

Efficient Water Quality Prediction Using Supervised Machine Learning

Efficient Water Quality Prediction Using Supervised Machine Learning

Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA

Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

Contact Info

Product

Resources

About