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

Hydrology ◽  
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.

scholarly journals Prediction of Odor Concentration Emitted from Wastewater Treatment Plant Using an Artificial Neural Network (ANN)

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 784
Author(s):  
Jeong-Hee Kang ◽  
JiHyeon Song ◽  
Sung Soo Yoo ◽  
Bong-Jae Lee ◽  
Hyon Wook Ji
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Prediction Accuracy ◽  
Treatment Plant ◽  
Ann Model ◽  
Odor Concentration ◽  
Odor Emission ◽  
Artificial Neural Network Ann

The odor emitted from a wastewater treatment plant (WWTP) is an important environmental problem. An estimation of odor emission rate is difficult to detect and quantify. To address this, various approaches including the development of emission factors and measurement using a closed chamber have been employed. However, the evaluation of odor emission involves huge manpower, time, and cost. An artificial neural network (ANN) is recognized as an efficient method to find correlations between nonlinear data and prediction of future data based on these correlations. Due to its usefulness, ANN is used to solve complicated problems in various disciplines of sciences and engineering. In this study, a method to predict the odor concentration in a WWTP using ANN was developed. The odor concentration emitted from a WWTP was predicted by the ANN based on water quality data such as biological oxygen demand, dissolved oxygen, and pH. The water quality and odor concentration data from the WWTP were measured seasonally in spring, summer, and autumn and these were used as input variations to the ANN model. The odor predicted by the ANN model was compared with the measured data and the prediction accuracy was estimated. Suggestions for improving prediction accuracy are presented.

scholarly journals Artificial Neural Network Model to Prediction of Eutrophication and Microcystis Aeruginosa Bloom

2020 ◽  
Vol 4 (2) ◽  
pp. 129-135 ◽  
Author(s):  
Pawalee Srisuksomwong ◽  
Jeeraporn Pekkoh
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chlorophyll A ◽  
Microcystis Aeruginosa ◽  
Quality Data ◽  
Water Quality Data ◽  
Severe Problem ◽  
Temperature Conductivity ◽  
Artificial Neural

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.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

2021 ◽  
Vol 03 (01) ◽  
pp. 45-52
Author(s):  
Hadjira Maouz ◽  
◽  
Asma Adda ◽  
Salah Hanini ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Thermal Aging ◽  
Training Data ◽  
Ethylene Propylene Diene Monomer ◽  
Ann Model ◽  
Ethylene Propylene ◽  
Artificial Neural ◽  
Input Variables ◽  
Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

scholarly journals Penerapan Algoritma Artificial Neural Network untuk Klasifikasi Opini Publik Terhadap Covid-19

2021 ◽  
Vol 5 (2) ◽  
pp. 109-118
Author(s):  
Euis Saraswati ◽  
Yuyun Umaidah ◽  
Apriade Voutama
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Artificial Neural Network ◽  
Text Mining ◽  
Ann Model ◽  
Rapid Spread ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
The Government ◽  
Artificial Neural Network Ann

Coronavirus disease (Covid-19) or commonly called coronavirus. This virus spreads very quickly and even almost infects the whole world, including Indonesia. A large number of cases and the rapid spread of this virus make people worry and even fear the increasing spread of the Covid-19 virus. Information about this virus has also been spread on various social media, one of which is Twitter. Various public opinions regarding the Covid-19 virus are also widely expressed on Twitter. Opinions on a tweet contain positive or negative sentiments. Sentiments of sentiment contained in a tweet can be used as material for consideration and evaluation for the government in dealing with the Covid-19 virus. Based on these problems, a sentiment analysis classification is needed to find out public opinion on the Covid-19 virus. This research uses Artificial Neural Network (ANN) algorithm with the Backpropagation method. The results of this test get 88.62% accuracy, 91.5% precision, and 95.73% recall. The results obtained show that the ANN model is quite good for classifying text mining.

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