Study on Development of Artificial Neural Network Forecasting Model Using Runoff, Water Quality Data

Maekuang reservoir is one of the water resources which provides water supply, livestock, and recreational in Chiangmai city, Thailand. The water quality and Microcystis aeruginosa are a severe problem in many reservoirs. M. aeruginosa is the most widespread toxic cyanobacteria in Thailand. Difficulty prediction for planning protects Maekuang reservoirs, the artificial Neural Network (ANN) model is a powerful tool that can be used to machine learning and prediction by observation data. ANN is able to learn from previous data and has been used to predict the value in the future. ANN consists of three layers as input, hidden, and output layer. Water quality data is collected biweekly at Maekuang reservoir (1999-2000). Input data for training, including nutrients (ammonium, nitrate, and phosphorus), Secchi depth, BOD, temperature, conductivity, pH, and output data for testing as Chlorophyll a and M. aeruginosa cells. The model was evaluated using four performances, namely; mean squared error (MSE), root mean square error (RMSE), sum of square error (SSE), and percentage error. It was found that the model prediction agreed with experimental data. C01-C08 scenarios focused on M. aeruginosa bloom prediction, and ANN tested for prediction of Chlorophyll a bloom shown on M01-M09 scenarios. The findings showed, this model has been validated for prediction of Chlorophyll a and shows strong agreement for nitrate, Log cell, and Chlorophyll a. Results indicate that the ANN can be predicted eutrophication indicators during the summer season, and ANN has efficient for providing the new data set and predict the behavior of M. aeruginosa bloom process.

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Application of an unsupervised artificial neural network technique to multivariant surface water quality data

Ecological Research ◽

10.1007/s11284-008-0495-z ◽

2008 ◽

Vol 24 (1) ◽

pp. 163-173 ◽

Cited By ~ 11

Author(s):

Özer Çinar ◽

Hasan Merdun

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Surface Water ◽

Surface Water Quality ◽

Quality Data ◽

Water Quality Data ◽

Artificial Neural

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Construction and Application of Recognition Model for Black-Odorous Water Bodies Based on Artificial Neural Network

Advances in Civil Engineering ◽

10.1155/2021/3918524 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Zhonghua Xu ◽

Changguo Dai ◽

Jing Wang ◽

Lejun Liu ◽

Lei Jiang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Water Resource Management ◽

Water Environment ◽

Water Bodies ◽

Quality Data ◽

Water Quality Data ◽

Recognition Model ◽

Digital Twins ◽

Artificial Neural

In the water environment, construction, and civil engineering industries, digital twins have gradually become a popular solution in recent years, and in digital twins, accurate data prediction and category recognition are important parts of it. Artificial neural network (ANN), a widely used data-driven model, can accurately identify nonlinear relationships in the water environment. In this paper, a recognition model for black-odorous water bodies based on ANN was established to directly identify the sensory description of water bodies. This study used water quality data and sensory description (color and odor) as samples to train backpropagation (BP) neural networks. The training results show that the accuracy of the color and odor models reaches 86.7% and 85.8%, respectively. It can thus be suggested that the sensory description can be accurately recognized by BP neural network. The application results indicate that all seven rivers had black-odorous phenomenon within a year. The recognition models have been instrumental in water resource management. Meanwhile, the models provide a reference for the evaluation and early warning of black-odorous water bodies in other regions.

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Predicting Runoff Chloride Concentrations in Suburban Watersheds Using an Artificial Neural Network (ANN)

Hydrology ◽

10.3390/hydrology7040080 ◽

2020 ◽

Vol 7 (4) ◽

pp. 80

Author(s):

Khurshid Jahan ◽

Soni M. Pradhanang

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Sodium Chloride ◽

Stormwater Runoff ◽

Quality Data ◽

Ann Model ◽

Water Quality Data ◽

Artificial Neural Network Ann ◽

Chloride Concentrations

Road salts in stormwater runoff, from both urban and suburban areas, are of concern to many. Chloride-based deicers [i.e., sodium chloride (NaCl), magnesium chloride (MgCl2), and calcium chloride (CaCl2)], dissolve in runoff, travel downstream in the aqueous phase, percolate into soils, and leach into groundwater. In this study, data obtained from stormwater runoff events were used to predict chloride concentrations and seasonal impacts at different sites within a suburban watershed. Water quality data for 42 rainfall events (2016–2019) greater than 12.7 mm (0.5 inches) were used. An artificial neural network (ANN) model was developed, using measured rainfall volume, turbidity, total suspended solids (TSS), dissolved organic carbon (DOC), sodium, chloride, and total nitrate concentrations. Water quality data were trained using the Levenberg-Marquardt back-propagation algorithm. The model was then applied to six different sites. The new ANN model proved accurate in predicting values. This study illustrates that road salt and deicers are the prime cause of high chloride concentrations in runoff during winter and spring, threatening the aquatic environment.

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Artificial Neural Network Based Water Quality Forecasting Model for Ganga River

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8841.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 2778-2785

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Research Work ◽

River Pollution ◽

Ganga River ◽

Forecasting Model ◽

Forecasting Accuracy ◽

Water Quality Forecasting ◽

Artificial Neural

Development of river water quality forecasting model (RWQFM) created using the concept of artificial neural network (ANN) for the river Ganga, India still has not been done as far as best awareness of the authors. In this research work an effort have been made for developing such model first time for the stream Ganga in the stretch from Devprayag to Roorkee, Uttarakhand, India by choosing five testing stations along this waterway. The month to month exploratory dataset for the time arrangement of 2001 to 2015 including four water quality parameters was taken. Using one of the proficient machine learning approach called ANN an optimal model is developed by conducting several experiments in Weka data mining tool. In advance the water quality is forecasted for next 12 months and the forecasting accuracy is determined using various performance measures. The computation of 12-steps ahead WQ indicated that the water comes out to be suitable for drinking throughout the year 2016 only at three stations: Devprayag, Rishikesh and Roorkee. At Haridwar station, the water is also comes out to be of best quality but only in nine months. In last quarter of 2016, a little degradation at Haridwar station while a crucial deterioration was noticed at Jwalapur site. The results showed that the proposed WQ model is more efficient in terms of the forecasting accuracy. At Rishikesh station the developed forecasting model achieved a noteworthy accuracy of 100%. Thus, the proposed ANN forecasting model is verified as an effective model and concluded that in overall the WQ of the Ganga River in this stretch is fine in 2016. Also, ANN has proven its significance as an efficient tool in the forecasting domain. Such models will definitely be helpful for the water management bodies in order to control the river pollution and consequently help the society as well

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Application of the Artificial Neural Network to monitor the quality of treated water

INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY ◽

10.24297/ijmit.v3i1.1388 ◽

2013 ◽

Vol 3 (1) ◽

pp. 38-45 ◽

Cited By ~ 9

Author(s):

Vahid Nourani ◽

Tohid Rezapour Khanghah ◽

Milad Sayyadi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Treatment Plant ◽

Quality Data ◽

Data Sets ◽

Water Quality Data ◽

Treated Water ◽

Engineering Sciences ◽

Artificial Neural

Due to importance of the quality of treated water as a drastic parameter in peoples life and engineering problems, numerous experimental and semi-experimental models were recently used by water and environmental engineers in order to estimate the quality of water. Between the used models, Artificial Neural Network (ANN) approach as an advantageous black box model was showed great authority in engineering sciences in general and in water engineering in particular. In this study, an ANN-based method was utilized to model the quality of the potable water parameters. To evaluate the model, the water quality data sets of Zarrineh Rood water treatment plant before and after treatment were used. After the statistical analysis on the recorded daily data sets, they were divided into calibration and verification sub-sets. In this paper the measured heat, PH, opacity, total hardness, and the level of calcium before the treatment process were considered as input variables of the model and the quantity of Total Dissolved Solids (TDS) and Electrical Conductivity (EC) after treatment were considered as output neurons of ANN. To have better interpretation about the model efficiency, the outcomes were compared with other classical and practical models and the results proved high merit of ANN in predicting the parameters of treated water.

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