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

2013 ◽  
Vol 3 (1) ◽  
pp. 38-45 ◽  
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.  

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

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.

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.

scholarly journals Predicting the liquefaction potential of soil layers in Tabriz city via artificial neural network analysis

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Mohammad Alizadeh Mansouri ◽  
Rouzbeh Dabiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soil Liquefaction ◽  
Data Sets ◽  
Liquefaction Potential ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Prediction Approach ◽  
Empirical Approaches

AbstractSoil liquefaction is a phenomenon through which saturated soil completely loses its strength and hardness and behaves the same as a liquid due to the severe stress it entails. This stress can be caused by earthquakes or sudden changes in soil stress conditions. Many empirical approaches have been proposed for predicting the potential of liquefaction, each of which includes advantages and disadvantages. In this paper, a novel prediction approach is proposed based on an artificial neural network (ANN) to adequately predict the potential of liquefaction in a specific range of soil properties. To this end, a whole set of 100 soil data is collected to calculate the potential of liquefaction via empirical approaches in Tabriz, Iran. Then, the results of the empirical approaches are utilized for data training in an ANN, which is considered as an option to predict liquefaction for the first time in Tabriz. The achieved configuration of the ANN is utilized to predict the liquefaction of 10 other data sets for validation purposes. According to the obtained results, a well-trained ANN is capable of predicting the liquefaction potential through error values of less than 5%, which represents the reliability of the proposed approach.

scholarly journals Applicability of Fuzzy Logic and Artificial Neural Network for Unpaved Airfield Surface Bearing Strength Prediction

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3373
Author(s):  
Ludek Cicmanec
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Regression Function ◽  
Large Data ◽  
Soil Strength ◽  
Data Sets ◽  
Bearing Strength ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The main objective of this paper is to describe a building process of a model predicting the soil strength at unpaved airport surfaces (unpaved runways, safety areas in runway proximity, runway strips, and runway end safety areas). The reason for building this model is to partially substitute frequent and meticulous inspections of an airport movement area comprising the bearing strength evaluation and provide an efficient tool to organize surface maintenance. Since the process of building such a model is complex for a physical model, it is anticipated that it might be addressed by a statistical model instead. Therefore, fuzzy logic (FL) and artificial neural network (ANN) capabilities are investigated and compared with linear regression function (LRF). Large data sets comprising the bearing strength and meteorological characteristics are applied to train the likely model variations to be subsequently compared with the application of standard statistical quantitative parameters. All the models prove that the inclusion of antecedent soil strength as an additional model input has an immense impact on the increase in model accuracy. Although the M7 model out of the ANN group displays the best performance, the M3 model is considered for practical implications being less complicated and having fewer inputs. In general, both the ANN and FL models outperform the LRF models well in all the categories. The FL models perform almost equally as well as the ANN but with slightly decreased accuracy.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

2019 ◽  
pp. 158-177
Author(s):  
Tamer Emara
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Saving ◽  
Ieee 802.16 ◽  
Feed Forward Neural Network ◽  
Artificial Neural ◽  
Adaptive Power ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

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