scholarly journals Artificial neural network system analysis and Kriging methodology for estimation of non-revenue water ratio

2020 ◽  
Vol 20 (5) ◽  
pp. 1871-1883 ◽  
Author(s):  
Eyüp Şişman ◽  
Burak Kizilöz
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
System Analysis ◽  
Kriging Model ◽  
Ann Model ◽  
Box Models ◽  
Neural Network System ◽  
Ann Models ◽  
Artificial Neural ◽  
Non Revenue Water

Abstract The non-revenue water (NRW) ratio parameter is significantly important for performance evaluation of water distribution systems. In order to evaluate the NRW ratio, the variables influencing this parameter should be determined. Therefore, the first aim of the paper is to define the variables which are influential on the estimation of the NRW ratio and then analyze these variables by using artificial neural networks (ANNs) methodology by means of 50 models with one, two, three, and four-variable input. Secondly, in this study, the NRW ratios have been predicted for the first time by using the Kriging methodology through only two variables. By using the data measured in 12 district meter areas (DMA) in Kocaeli, 60 models in total have been established for NRW ratio prediction through the ANN and Kriging methodologies. The ANN models are closed-box models and therefore the interpretation of the ANN model results requires higher expert opinion. As a consequence, the results show that Kriging model graphs produce much more useful information than ANN models in terms of application and interpretation.

Prediction of multi-components (chlorine, biomass and substrate concentrations) in water distribution systems using artificial neural network (ANN) models

2009 ◽  
Vol 9 (3) ◽  
pp. 289-297
Author(s):  
Celia D. D'Souza ◽  
M. S. Mohan Kumar
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Transport Model ◽  
Absolute Error ◽  
Temporal Variations ◽  
Residual Chlorine ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural

Artificial Neural Networks (ANN) models are used to predict residual chlorine, substrate and biomass concentrations in a Water Distribution System (WDS). ANN models with different architectures are developed: a one output ANN model (predicting chlorine, substrate and biomass individually), a two output ANN model (predicting chlorine + substrate, chlorine + biomass or substrate + biomass) and a three output ANN model (chlorine + substrate + biomass). This study is carried out for the Bangalore City and North Marin WDSs. Data for these WDSs is obtained from the multi-component reaction transport model. The models are compared using the correlation coefficient (R) and the Mean Absolute Error (MAE). The models developed are able to predict, reasonably well, the temporal variations in the chlorine, substrate and biomass concentrations. Error analysis is carried out to determine the robustness of the models.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

scholarly journals Fast, Spectroscopy-Based Prediction of In Vitro Dissolution Profile of Extended Release Tablets Using Artificial Neural Networks

Pharmaceutics ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 400 ◽  
Author(s):  
Galata ◽  
Farkas ◽  
Könyves ◽  
Mészáros ◽  
Szabó ◽  
...  
Keyword(s):  
Raman Spectra ◽  
Extended Release ◽  
Near Infrared ◽  
Hydroxypropyl Methylcellulose ◽  
In Vitro Dissolution ◽  
Dissolution Profile ◽  
Ann Model ◽  
Compression Force ◽  
Ann Models ◽  
Artificial Neural

The pharmaceutical industry has never seen such a vast development in process analytical methods as in the last decade. The application of near-infrared (NIR) and Raman spectroscopy in monitoring production lines has also become widespread. This work aims to utilize the large amount of information collected by these methods by building an artificial neural network (ANN) model that can predict the dissolution profile of the scanned tablets. An extended release formulation containing drotaverine (DR) as a model drug was developed and tablets were produced with 37 different settings, with the variables being the DR content, the hydroxypropyl methylcellulose (HPMC) content and compression force. NIR and Raman spectra of the tablets were recorded in both the transmission and reflection method. The spectra were used to build a partial least squares prediction model for the DR and HPMC content. The ANN model used these predicted values, along with the measured compression force, as input data. It was found that models based on both NIR and Raman spectra were capable of predicting the dissolution profile of the test tablets within the acceptance limit of the f2 difference factor. The performance of these ANN models was compared to PLS models using the same data as input, and the prediction of the ANN models was found to be more accurate. The proposed method accomplishes the prediction of the dissolution profile of extended release tablets using either NIR or Raman spectra.

scholarly journals Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Hung Vo Thanh ◽  
Yuichi Sugai ◽  
Kyuro Sasaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oil Recovery ◽  
Early Stage ◽  
Computational Time ◽  
Residual Oil ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
And Storage

Abstract Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

scholarly journals Optimization and Assessment of Residual Chlorine using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Modeling

2019 ◽  
Vol 8 (3) ◽  
pp. 258-263
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Distribution System ◽  
Water Distribution ◽  
Residual Chlorine ◽  
Urban Water ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Rsm Model

Water polluted with microorganisms and pathogens is one of the most significant hazards to public health. Potential microorganisms unsafe to human health can be destroyed through effective disinfection. To stop the re-growth of microorganisms, it is also advisable to take care of the residual disinfectant in the water distribution networks. The most frequently used cleanser material is chlorine. When the chlorine dosage is too low, there will be a deficiency of enough residues at the end of the water network system, leading to re-growth of microorganisms. Addition of an excessive amount of chlorine will lead to corrosion of the pipeline network and also the development of disinfection by-products (DBPs) including carcinogens. Thus, to determine the best rate of chlorine dosage, it is essential to model the system to forecast chlorine decay within the network. In this research study, two major modeling and optimization strategies were employed to assess the optimum dosage of chlorine for municipal water disinfection and also to predict residual chlorine at any predetermined node within the water distribution network. Artificial neural network (ANN) modeling techniques were used to forecast chlorine concentrations in different nodes in the urban water distribution system in Muscat, the capital of the Sultanate of Oman. One-year dataset from one of the distribution system was used for conducting network modeling in this study. The input factors to RSM model considered were pH, chlorine dosage and time. Response variables for RSM model were fixed as total organic carbon (TOC), Biological oxygen demand (BOD) and residual chlorine An Artificial neural network (ANN) model for residual chlorine was created with pH, inlet-concentration of chlorine and initial temperature as input parameters and residual chlorine in the piping network as an output parameter. The ANN model created using these data can be employed to forecast the residual chlorine value in the urban water network at any given specific location. The results from this study utilizing the uniqueness of an ANN model to predict residual chlorine and water quality parameters have the potential to detect complex, higher-order behavior between input and output parameters exist in urban water distribution system.

scholarly journals Prediction of Rate of Penetration for wells at Nam Con Son basin using Artificial Neural Networks models

10.29007/4sdt ◽  
2022 ◽  
Author(s):  
Vu Khanh Phat Ong ◽  
Quang Khanh Do ◽  
Thang Nguyen ◽  
Hoang Long Vo ◽  
Ngoc Anh Thy Nguyen ◽  
...  
Keyword(s):  
Network Model ◽  
Oil And Gas ◽  
Research Approach ◽  
Ann Model ◽  
Rate Of Penetration ◽  
Drilling Parameters ◽  
Drilling Operations ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The rate of penetration (ROP) is an important parameter that affects the success of a drilling operation. In this paper, the research approach is based on different artificial neural network (ANN) models to predict ROP for oil and gas wells in Nam Con Son basin. The first is the process of collecting and evaluating drilling parameters as input data of the model. Next is to find the network model capable of predicting ROP most accurately. After that, the study will evaluate the number of input parameters of the network model. The ROP prediction results obtained from different ANN models are also compared with traditional models such as the Bingham model, Bourgoyne & Young model. These results have shown the competitiveness of the ANN model and its high applicability to actual drilling operations.

Estimation of Groundwater Level Using Artificial Neural Networks: a Case Study of Hatay-Turkey

2017 ◽  
Author(s):  
Fatih Üneş ◽  
Mustafa Demirci ◽  
Eyup Ispir ◽  
Yunus Ziya Kaya ◽  
Mustafa Mamak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Groundwater Level ◽  
Underground Water ◽  
Population Increase ◽  
Ann Model ◽  
Total Water Consumption ◽  
Ann Models ◽  
Artificial Neural

Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and temperature observations of Turkish General Directorate of State Hydraulic Works (DSI) in Hatay, Amik Plain, Kumlu district were used between 2000 and 2015 years. The performance evaluation was done by creating Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) models. The ANN model gave better results than the MLR model.

Sign in / Sign up

Export Citation Format

Share Document