neural network modeling Articles
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Assessing model efficacy in forecasting EPS of Chinese firms using fundamental accounting variables: a comparative study
In this paper, we compare the forecasting accuracy of two neural network models in forecasting earnings per share of Chinese listed companies based upon fundamental accounting variables. In one neural network model, weights estimated by back propagation were utilised, and in the other model a genetic algorithm was utilised. Based upon a sample of 723 Chinese companies in 22 industries over a ten ...
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Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone
Present paper endeavors to develop predictive artificial neural network model for forecasting the mean monthly total ozone concentration over Arosa, Switzerland. Single hidden layer neural network models with variable number of nodes have been developed and their performances have been evaluated using the method of least squares and error estimation. Their performances have been compared with ...
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Alternate neural network models in decision making for socio-economic development
Alternate neural network models are used to identify the structure of preferences for development alternatives and their consequences in the cases of Doon Valley and National Capital Region in India in the context of carrying-capacity-based developmental planning. Alternate neural network models are presented as an effective alternative to deal with multi-criteria decision-making situations. The ...
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An intelligent neural network model for evaluating performance of immobilized cell biofilter treating hydrogen sulphide vapors
Biofiltration has shown to be a promising technique for handling malodours arising from process industries. The present investigation pertains to the removal of hydrogen sulphide in a lab scale biofilter packed with biomedia, encapsulated by sodium alginate and poly vinyl alcohol. The experimental data obtained under both steady state and shock loaded conditions were modelled using the basic ...
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Enhancing accuracy of autoregressive time series forecasting with input selection and wavelet transformation
Autoregressive time series forecasting is common in different areas within water resources, which include hydrology, ecology, and the environment. Simple forecasting models such as linear regression have the advantage of fast runtime, which is attractive for real-time forecasting. However, their forecasting performance might not be acceptable when a non-linear relationship exists between ...
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Fuzzy Logic and Artificial neural network approaches for dissolved oxygen prediction
A study on application of data-driven models namely the rule-based model based on mamdani Fuzzy Logic and Artificial Neural Network model in predicting dissolved oxygen in an effluent-impacted urban river is presented and compared. Combined rule bases were formed from the generated fuzzy rules for input – output mapping. Predictability of both the models was good with better performance for ...
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A hybrid model coupled with singular spectrum analysis for daily rainfall prediction
A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. ...
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Daily river flow forecasting using wavelet ANN hybrid models
Advance time step stream flow forecasting is of paramount importance in controlling flood damage. During the past few decades, artificial neural network (ANN) techniques have been used extensively in stream flow forecasting and have proven to be a better technique than other forecasting methods such as multiple regression and general transfer function models. This study uses discrete wavelet ...
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Prediction of lake eutrophication using artificial neural networks
An artificial neural network (ANN), which is a data–driven modelling approach, is proposed to indicate the water quality of Lake Fuxian, the deepest lake of southwest China. To determine the nonlinear relationships between the water quality factors and eutrophication indicators, several ANN models were chosen. The back–propagation and radial basis function neural network models were applied to ...
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A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity
The availability of accurate empirical models for multi-step-ahead (MS) prediction is desirable in many areas. Some ANN technologies, such as multiple-neural network, time-delay neural network (TDNN), and adaptive time-delay neural network (ATNN), have proven successful in addressing various complicated problems. The purpose of this study was to investigate the applicability of neural network MS ...
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Research on prediction of tourists' quantity in Jiuzhaigou Valley scenic based on ABR@G integration model
As the uncertain changes of tourists' quantity have challenged scenic management, which affects the environmental pollution, many researches confirm that forecasting, which is the foundation of the tourists' management can provide guarantee of effective environment protection. Because the changes of tourists' quantity with complex characteristics of the linear and non–linear are mutually ...
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Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through
k -fold testingThe improvement of methods for estimating reference evapotranspiration (ET0) requiring few climatic inputs is crucial, due to the partial or total lack of climatic inputs in many situations. The current paper compares the effect of local and external training procedures in neuro-fuzzy and neural network models for estimating ET0 relying on two input combinations considering k-fold testing. ...
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Development of a discharge equation for side weirs using artificial neural networks
ABSTRACT Flow over a side weir is one of the more complex flows to simulate in one-dimensional unsteady flow analysis. Various experiments have been applied, but no agreement is apparent in the literature about the best method. In this study, an Artificial Neural Network model has been used to extract a discharge equation for side weirs which accurately estimates overflow discharges. The ...
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Neural network model for identification of societal preference of environmental issues
A new method for identification of preferences of environmental issues using the societal approach is suggested. The preferences assigned by different economic groups to 11 environmental issues are obtained through analysis of linguistically stated relative rankings using fuzzy partial ordering method. The system identification technique based on neural networks is used to identify a logical ...
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Grade estimation of ore stockpiles by using Artificial Neural Networks: case study on Choghart Iron Mine in Iran
This paper investigates the application of the neural network in a run of mine ore stockpile in Choghart Iron Mine of Iran. While a significant amount of high grade stockpile at Choghart mine, near the open pit, is an environmental hazard, it is also potential source of high grade ores. For future exploitation, determination of stockpile resource tonnage and grade has become an important aspect ...
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Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media
The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for ...
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Ground–level ozone prediction using a neural network model based on meteorological variables and appiled to the metropolitan area of São Paulo
A neural network model to predict ozone concentration in the São Paulo Metropolitan Area was developed, based on average values of meteorological variables in the morning (8:00–12:00 hr) and afternoon (13:00–17:00 hr) periods. Outputs are the maximum and average ozone concentrations in the afternoon (12:00–17:00 hr). The correlation coefficient between computed and measured values was 0.82 and ...
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Building the foundation for Prudhoe Bay oil production optimisation using neural networks
Field data from the Prudhoe Bay oil field in Alaska was used to develop a neural network model of the cross-country gas transit pipeline network between the production separation facilities and central gas compression plant. The trained model was extensively tested and verified using 30% of the data that was not used during the training process. The results show good accuracy in reproducing the ...
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Improving applicability of neuro-genetic algorithm to predict short-term water level: a case study
This paper proposes a practical approach of a neuro-genetic algorithm to enhance its capability of predicting water levels of rivers. Its practicality has three attributes: (1) to easily develop a model with a neuro-genetic algorithm; (2) to verify the model at various predicting points with different conditions; and (3) to provide information for making urgent decisions on the operation of ...
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Coupling of neural network and dispersion models: a novel methodology for air pollution models
Supervised neural net models and dispersion models are two important approaches for evaluating air pollution concentrations. The authors propose the development of an integrated model, in order to optimise the performances of each methodology. The concentrations evaluated by an air pollution model are coupled with a Neural Net (NN), so as to adjust the influence of important variables on ...
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