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 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 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 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 Application of Multiple Linear Regression and Artificial Neural Networks for the Prediction of the Packing and Capsule Filling Performance of Coated and Plain Pellets Differing in Density and Size

Pharmaceutics ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 244 ◽  
Author(s):  
Panagiotis Barmpalexis ◽  
Ioannis Partheniadis ◽  
Konstantina-Sepfora Mitra ◽  
Miltiadis Toskas ◽  
Labrini Papadopoulou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Theoretical Data ◽  
Ann Model ◽  
Pellet Size ◽  
Weight Variation ◽  
Pellet Density ◽  
Artificial Neural

Plain or coated pellets of different densities 1.45, 2.53, and 3.61 g/cc in two size ranges, small (380–550 μm) and large (700–1200 μm) (stereoscope/image analysis), were prepared according to experimental design using extrusion/spheronization. Multiple linear regression (MLR) and artificial neural networks (ANNs) were used to predict packing indices and capsule filling performance from the “apparent” pellet density (helium pycnometry). The dynamic packing of the pellets in tapped volumetric glass cylinders was evaluated using Kawakita’s parameter a and the angle of internal flow θ. The capsule filling was evaluated as maximum fill weight (CFW) and fill weight variation (FWV) using a semi-automatic machine that simulated filling with vibrating plate systems. The pellet density influenced the packing parameters a and θ as the main effect and the CFW and FWV as statistical interactions with the coating. The pellet size and coating also displayed interacting effects on CFW, FWV, and θ. After coating, both small and large pellets behaved the same, demonstrating smooth filling and a low fill weight variation. Furthermore, none of the packing indices could predict the fill weight variation for the studied pellets, suggesting that the filling and packing of capsules with free-flowing pellets is influenced by details that were not accounted for in the tapping experiments. A prediction could be made by the application of MLR and ANNs. The former gave good predictions for the bulk/tap densities, θ, CFW, and FWV (R-squared of experimental vs. theoretical data >0.951). A comparison of the fitting models showed that a feed-forward backpropagation ANN model with six hidden units was superior to MLR in generalizing ability and prediction accuracy. The simplification of the ANN via magnitude-based pruning (MBP) and optimal brain damage (OBD), showed good data fitting, and therefore the derived ANN model can be simplified while maintaining predictability. These findings emphasize the importance of pellet density in the overall capsule filling process and the necessity to implement MLR/ANN into the development of pellet capsule filling operations.

scholarly journals Comparative performance of multiple linear regression and artificial neural network models in estimating solute-transport parameters

2021 ◽  
Vol 18 (1) ◽  
pp. 27
Author(s):  
Mohammad Abdul Mojid ◽  
A.B.M. Zahid Hossain
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Solute Transport ◽  
Multiple Linear Regression ◽  
Ann Model ◽  
Transport Parameters ◽  
Ann Models ◽  
Artificial Neural ◽  
Mlr Model

Indirect estimate of solute-transport parameters through pedo-transfer functions (PTFs) is becoming important due to expensive and time-consuming direct measurement of the parameters for a large number of soils and solutes. This study evaluated the relative performance of PTFs of multiple linear regression (MLR) and Artificial Neural Network (ANN) models in predicting velocity (<em>V</em>), dispersion coefficient (<em>D</em>) and retardation factor (<em>R</em>) of CaCl<sub>2</sub>, NaAsO<sub>2</sub>, Cd(NO<sub>3</sub>)<sub>2</sub>, Pb(NO<sub>3</sub>)<sub>2</sub> and C<sub>9</sub>H<sub>9</sub>N<sub>3</sub>O<sub>2</sub> (carbendazim) in five agricultural soils. <em>V</em>, <em>D</em> and <em>R</em> of the solutes were determined in repacked soil columns under steady-state unsaturated water flow conditions. Textural class, particle size distribution, bulk density, organic carbon, relative pH, clay%, grain size, and uniformity coefficient of the soils were determined. MLR and ANN models were calibrated with the measured data of four soils and verified for another soil. Root-Mean Square Error (RMSE) is significantly smaller (0.015) and modelling efficiency (EF) is significantly larger (0.999) for ANN model than those (0.096 and 0.954, respectively) for MLR model. Negative Mean Absolute Error (MAE) (-0.0002) of MLR model indicates overestimation, while positive MAE (0.00003) of ANN model indicates minimal underestimation. The ANN model is less biased than the MLR model during prediction. Thus, the ANN model can significantly enhance pollution transport prediction through soils with good accuracy.

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