scholarly journals Application of Multiple Linear Regression Models and Artificial Neural Networks on the Surface Ozone Forecast in the Greater Athens Area, Greece

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. P. Moustris ◽  
P. T. Nastos ◽  
I. K. Larissi ◽  
A. G. Paliatsos
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Surface Ozone ◽  
Daily Maximum ◽  
Linear Regression Models ◽  
Ann Model ◽  
Ozone Forecast ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Athens Area

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone’s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.

scholarly journals Estimation of soil aggregate stability indices using artificial neural network ‎and multiple linear regression models

2017 ◽  
Vol 7 ◽  
Author(s):  
Maryam Marashi ◽  
Ali Mohammadi Torkashvand ◽  
Abbas Ahmadi ◽  
Mehrdad Esfandyari
Keyword(s):  
Fractal Dimension ◽  
Linear Regression ◽  
Soil Properties ◽  
Multiple Linear Regression ◽  
Aggregate Stability ◽  
Pedotransfer Functions ◽  
Linear Regression Models ◽  
Ann Model ◽  
Intelligence System ◽  
Artificial Neural

During recent decades, an artificial intelligence system has been used for developing the pedotransfer functions (PTFs) for estimation of soil properties. In the present study, the capabilities of multiple linear regression (MLR) and artificial neural networks (ANNs) in developing PTFs for estimating mean weight diameter (MWD) from routine soil properties (P<sub>1</sub>) and combination of routine soil properties and fractal dimension of aggregates (P<sub>2</sub>) were evaluated. The results showed that the ANN model for estimating MWD is more accurate than the MLR model. Application of fractal dimension of aggregates as a predictor in both methods improved the accuracy of PTFs.

scholarly journals An Empirical Comparison of Multiple Linear Regression and Artificial Neural Network for Concrete Dam Deformation Modelling

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Mingjun Li ◽  
Junxing Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Prediction Accuracy ◽  
Ann Model ◽  
Concrete Dam ◽  
Artificial Neural ◽  
Dam Deformation ◽  
Artificial Neural Network Ann

Deformation predicting models are essential for evaluating the health status of concrete dams. Nevertheless, the application of the conventional multiple linear regression model has been limited due to the particular structure, random loading, and strong nonlinear deformation of concrete dams. Conversely, the artificial neural network (ANN) model shows good adaptability to complex and highly nonlinear behaviors. This paper aims to evaluate the specific performance of the multiple linear regression (MLR) and artificial neural network (ANN) model in characterizing concrete dam deformation under environmental loads. In this study, four models, namely, the multiple linear regression (MLR), stepwise regression (SR), backpropagation (BP) neural network, and extreme learning machine (ELM) model, are employed to simulate dam deformation from two aspects: single measurement point and multiple measurement points, approximately 11 years of historical dam operation records. Results showed that the prediction accuracy of the multipoint model was higher than that of the single point model except the MLR model. Moreover, the prediction accuracy of the ELM model was always higher than the other three models. All discussions would be conducted in conjunction with a gravity dam study.

scholarly journals Development of artificial neural network and multiple linear regression models in the prediction process of the hot mix asphalt properties

2017 ◽  
Vol 44 (12) ◽  
pp. 994-1004 ◽  
Author(s):  
Ivica Androjić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Models ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Linear Regression Models ◽  
Artificial Neural ◽  
Multiple Linear Regression Models

The oscillation of asphalt mix composition on a daily basis significantly affects the achieved properties of the asphalt during production, thus resulting in conducting expensive laboratory tests to determine existing properties and predicting the future results. To decrease the amount of such tests, a development of artificial neural network and multiple linear regression models in the prediction process of predetermined dependent variables air void and soluble binder content is presented. The input data were obtained from a single laboratory and consists of testing 386 mixes of hot mix asphalt (HMA). It was found that it is possible and desirable to apply such models in the prediction process of the HMA properties. The final aim of the research was to compare results of the prediction models on an independent dataset and analyze them through the boundary conditions of technical regulations and the standard EN 13108-21.

scholarly journals Prediction of Sediment Accumulation Model for Trunk Sewer Using Multiple Linear Regression and Neural Network Techniques

2019 ◽  
Vol 5 (1) ◽  
pp. 82 ◽  
Author(s):  
Rami Raad Ahmed Al-Ani ◽  
Basim Hussein Khudair Al-Obaidi
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sediment Accumulation ◽  
Multiple Linear Regression Model ◽  
Sediment Deposition ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Sewer Rehabilitation ◽  
Artificial Neural Network Ann

Sewer sediment deposition is an important aspect as it relates to several operational and environmental problems. It concerns municipalities as it affects the sewer system and contributes to sewer failure which has a catastrophic effect if happened in trunks or interceptors. Sewer rehabilitation is a costly process and complex in terms of choosing the method of rehabilitation and individual sewers to be rehabilitated.  For such a complex process, inspection techniques assist in the decision-making process; though, it may add to the total expenditure of the project as it requires special tools and trained personnel. For developing countries, Inspection could prohibit the rehabilitation proceeds. In this study, the researchers proposed an alternative method for sewer sediment accumulation calculation using predictive models harnessing multiple linear regression model (MLRM) and artificial neural network (ANN). AL-Thawra trunk sewer in Baghdad city is selected as a case study area; data from a survey done on this trunk is used in the modeling process. Results showed that MLRM is acceptable, with an adjusted coefficient of determination (adj. R2) in order of 89.55%. ANN model found to be practical with R2 of 82.3% and fit the data better throughout its range. Sensitivity analysis showed that the flow is the most influential parameter on the depth of sediment deposition.

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