scholarly journals Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

2020 ◽  
Vol 15 (5) ◽  
pp. 647-652
Author(s):  
Sarmad Dashti Latif ◽  
Muhammad Shukri Bin Nor Azmi ◽  
Ali Najah Ahmed ◽  
Chow Ming Fai ◽  
Ahmed El-Shafie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Ann Model ◽  
Artificial Neural ◽  
Feitsui Reservoir

Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

Water Quality Forecast Based on BP-Artificial Neural Network Model in Qiantang River

2014 ◽  
Vol 668-669 ◽  
pp. 994-998
Author(s):  
Jin Ting Ding ◽  
Jie He
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Ph Value ◽  
Back Propagation ◽  
Ammonia Nitrogen ◽  
Quality Parameters ◽  
Ann Model ◽  
Qiantang River ◽  
Artificial Neural

This study aims at providing a back propagation-artificial neural network (BP-ANN) model on forecasting the water quality change trend of Qiantang River basin. To achieve this goal, a three-layer (one input layer, one hidden layer, and one output layer) BP-ANN with the LM regularization training algorithm was used. Water quality variables such as pH value, dissolved oxygen, permanganate index and ammonia-nitrogen was selected as the input data to obtain the output of the neural network. The ANN structure with 17 hidden neurons obtained the best selection. The comparison between the original measured and forecast values of the ANN model shows that the relative errors, with a few exceptions, were lower than 9%. The results indicated that the BP neural network can be satisfactorily applied to forecast precise water quality parameters and is suitable for pre-alarm of water quality trend.

Novel application of a back-propagation artificial neural network model formulated to predict algal bloom

1997 ◽  
Vol 36 (5) ◽  
pp. 89-97 ◽  
Author(s):  
Ken-ichi Yabunaka ◽  
Masaaki Hosomi ◽  
Akihiko Murakami
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Algal Bloom ◽  
Back Propagation ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ann Model ◽  
Chlorophyll A Concentration

This paper describes the novel application of an artificial neural network (ANN) model based on the back-propagation method formulated to predict algal bloom by simulating the future growth of five phytoplankton species and the chlorophyll a concentration in the second largest lake in Japan: eutrophic freshwater Lake Kasumigaura. Comparison of observed and calculated values showed that (i) seasonal variations in the biomass of Microcystis spp. were well-predicted with respect to the timing and magnitude of algal bloom, and (ii) the concentration of chlorophyll a, as an indicator of the total biomass of phytoplankton, was well predicted in general. The resultant correlations for the other species, however, showed that model learning was insufficient to effectively predict species biomass; thereby indicating that some unknown factors which are not represented by the set of water quality parameters used as model input data affect phytoplankton growth. A sensitivity analysis performed on input parameters showed that chlorophyll a concentration was mainly affected by PO4-P concentration, while cyanobacteria and diatom species were affected by NO3-N and NH4-N concentrations, respectively. These results indicate that the “algal bloom” ANN model achieved reasonable effectiveness with respect to learning the relationship between the selected water quality parameters and algal bloom.

scholarly journals Forecasting water quality parameters using artificial neural network for irrigation purposes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. I. Ubah ◽  
L. C. Orakwe ◽  
K. N. Ogbu ◽  
J. I. Awu ◽  
I. E. Ahaneku ◽  
...  
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Performance Evaluation ◽  
Water Quality Index ◽  
Quality Index ◽  
Model Performance ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Artificial Neural

AbstractThis study was aimed at analyzing the water quality of Ele River Nnewi, Anambra State for irrigation purposes with a view to predicting a one-year water quality index using Artificial Neural Network (ANN). Water pollution has posed a major problem and identifying the points of pollution in the River system is a very difficult task. To overcome this task, the need to determine the pollution level arose by modeling and predicting four water quality parameters at four (4) different locations using the Artificial Neural Network. These parameters include the pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Sodium (Na), respectively. The water quality results showed that the pH values which ranges from 6.01 to 6.87 were within the FAO standard in all the points for both rainy and dry seasons, whereas the TDS (mg/l), EC (dS/m) and Na (mg/l) parametric values range from 2001 to 2506, 3.01 to 5.76, and 40.42 to 73.45 respectively, were above the FAO standard from point 1 to point 3 and falls within the FAO standard at point 4 with values ranging from 1003 to 1994, 2.01 to 2.78 and 31.24 to 39.44, respectively. However, during the dry season, the TDS, EC, and Na values range from 2002 to 2742, 3.04 to 5.82 and 40.14 to 88.45 respectively, were all above the FAO standard. Generally, the artificial neural network modeled the actual water quality data set very well with good prediction. The training model performance evaluation shows that the R2 values ranges from 0.981 to 0.990, 0.981 to 0.988, 0.981 to 0.989 and 0981 to 0.989, for pH, TDS, EC, and Na. The testing model performance shows that the R2 value ranges from 0.952 to 0.967, 0.953 to 0.970, 0.951 to 0.967and 0.953 to 0.968, for pH, TDS, EC and Na while the forecast performance evaluation shows that the R2 values ranges from 0.945 to 0.968, 0.946 to 0.968, 0.944 to 0.967 and 0.949 to 0.965 for pH, TDS, EC and Na respectively. It was also observed that the Root Mean Squared Error (RMSE) ranges from 0.022 to 0.088, 0.012 to 0.087, 0.015 to 0.085 and 0.014 to 0.084 for pH, TDS, EC and Na, respectively. Information from this study will serve as a guide to researchers on the water quality index for irrigation purposes. Also, it will guide the government and agencies on policy, management and decision-making on water resources.

scholarly journals Prediction of Groundwater Quality Using Artificial Neural Network

2021 ◽  
Author(s):  
Rita Maria Joseph ◽  
Alna D Manjaly ◽  
Sreeram Unni ◽  
Able E C ◽  
Vinitha Sharon
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Groundwater Quality ◽  
Quality Criterion ◽  
Quality Parameters ◽  
Total Hardness ◽  
Target Values ◽  
Input Parameters ◽  
Artificial Neural

Assessment and prediction of water quality is a vital tool for the management of water resources systems. It is necessitous for human existence, agriculture and industry. This project delves into the prediction of groundwater quality parameters and groundwater quality criterion based on the Artificial Neural Network Modelling with the study area as Kerala, a state of India. Two models were developed. The first model employs the water quality parameters such as pH, electrical conductivity, total hardness as the input parameters and calcium, magnesium, chloride, fluoride, nitrate concentration as the output parameters. The second model was designed by giving input as, input values and the predicted output values of the first model, and groundwater quality criterion corresponding to each location as the target values. The output qualitative parameters were estimated and compared with the measured values, to evaluate the influence of key input parameters. The number of neurons to be given in the hidden layer was decided by the trial-and-error method. Data of 506 water samples from all over Kerala were collected for modelling.

scholarly journals Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1782
Author(s):  
Elias Eze ◽  
Sarah Halse ◽  
Tahmina Ajmal
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Mean Square Error ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
The Novel ◽  
Mean Square ◽  
Hybrid Prediction

Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.

Application of Artificial Neural Network to Predict the Water Quality Index of Brahmani River Basin Odisha, India

2018 ◽  
Vol 6 (8) ◽  
pp. 205
Author(s):  
Binayini Bhagat ◽  
D. P. Satapathy
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Surface Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Physico Chemical ◽  
Artificial Neural

Water is one of the prime elements responsible for subsistence on the earth. The scarcity of potable water is gradually increasing with the increase in population. The surface water quality is a very crucial and sensitive issue and is also a great environmental concern worldwide. Surface water pollution by physical, chemical, radiological and biological contaminants can be considered as an epidemic at times, all over the world. The present research work aims at assessing the water quality index (WQI) in the surface water of Brahmani river basin in Odisha by monitoring five sampling locations. The surface water samples data were subjected to comprehensive physico-chemical analysis besides general parameters. The monthly water quality parameters were collected and analyzed from five selected gauging stations of Odisha during the months of January to December from 2011 to 2016. Eleven physical, chemical and biological water quality parameters viz. pH, Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Electrical Conductivity(EC), Nitrogen as nitrate (Nitrate-N), Total Coli-form Bacteria(TC), Fecal Coli-form Bacteria(FC), Chemical Oxygen Demand (COD), Nitrogen as ammonia (NH4-N), Total Alkalinity (TA) as CaCO3, Total Hardness (TH) as CaCO3 were selected for the analysis. Analysis of water quality for Brahmani River is done by Water Quality Index (WQI). Prediction of water quality index is done by using Artificial Neural Network (ANN).  It is apparent from WQI values that Talcher and Panposh recorded the water quality as moderate to poor and nearly unsuitable during the years 2011-2016 indicating water as not safe for domestic purposes and needs treatment, the WQI values of Kamalanga ranged from good to poor and the WQI values of Aul and Pottamundai ranged from good to moderate. Eleven physico-chemical parameters were involved in this analysis as input variables and water quality index as output variable. Two models were proposed to identify the most effective model in an attempt to predict the WQI.  Correlation between the parameters was carried out to find out the significant parameters affecting WQI. The ANN developed was trained and tested successfully using the available data sets and the performance of ANN models were determined by coefficient of determination (R2) and Root Mean Square Error (RMSE). Results show that ANN-1 gives the higher value of R2 in summer, monsoon and winter season (0.989, 0.976 and 0.959) and low RMSE (2.1865, 2.0768 and1.9657) as compared to that of the second model (ANN-2) which gives R2 value as 0.933, 0.945 and 0.943 and RMSE value as 2.8765, 2.5456 and 1.2745 for summer, monsoon and winter seasons respectively. Hence this study triggered the use of Artificial Neural Network to predict the Water Quality Index (WQI) rather than using the traditional WQI equation.

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