Estimativa de escoamento em captação urbana utilizando modelos de rede neural artificial

Many types of physical models have been developed for runoff estimation with successful results. However, accurate runoff estimation remains a challenging problem owing to the lack of field data and the complexity of its hydrological process. In this paper, a machine learning method for runoff estimation is presented as an alternative approach to the physical model. Various types of input variables and Artificial Neural Network (ANN) architectures were examined in this study. Results showed that a two-layer network with the tansig activation function and the Levenberg–Marquardt learning algorithm had the best performance. For this architecture, the most effective input vector consists of a catchment perimeter, canal length, slope, runoff coefficient, and rainfall intensity. However, results of multivariate analysis of variance indicated the significant interaction effect of input data and the ANN architecture. Thus, to create a suitable ANN model for runoff estimation, a systematic determination of the input vector is necessary.

Download Full-text

RUNOFF ESTIMATION IN URBAN CATCHMENT USING ARTIFICIAL NEURAL NETWORK MODELS

PLANT ARCHIVES ◽

10.51470/plantarchives.2021.v21.s1.371 ◽

2020 ◽

Vol 21 (Supplement 1) ◽

Author(s):

Baharak Motamedvaziri ◽

Payam Najafi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Learning Algorithm ◽

Input Vector ◽

Physical Models ◽

Accurate Estimation ◽

Hydrological Process ◽

Neural Network Models ◽

Study Results ◽

Artificial Neural

Many types of physical models have been developed for runoff estimation with successful results. However, accurate estimation of runoff remains a challenging problem owing to the lack of field data and the complexity of its hydrological process. In this paper, a machine learning method for runoff estimation is presented as an alternative approach to the physical model. Various types of input variables and artificial neural network (ANN) architectures were examined in this study. Results showed that a two-layer network with the tansig activation function and the Levenberg–Marquardt learning algorithm performed the best. For this architecture, the most effective input vector consists of a catchment perimeter, canal length, slope, runoff coefficient, and rainfall intensity. However, results of multivariate analysis of variance indicated the significant interaction effect of input data and the ANN architecture. Thus, to create a suitable ANN model for runoff estimation, a systematic determination of the input vector is necessary

Download Full-text

Monitoring-System Development of Gillnet Using Artificial Neural Network

Volume 6B: Ocean Engineering ◽

10.1115/omae2020-19011 ◽

2020 ◽

Author(s):

Chungkuk Jin ◽

HanSung Kim ◽

JeongYong Park ◽

MooHyun Kim ◽

Kiseon Kim

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Big Data ◽

Learning Algorithm ◽

System Development ◽

Ann Model ◽

Ann Models ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

Abstract This paper presents a method for detecting damage to a gillnet based on sensor fusion and the Artificial Neural Network (ANN) model. Time-domain numerical simulations of a slender gillnet were performed under various wave conditions and failure and non-failure scenarios to collect big data used in the ANN model. In training, based on the results of global performance analyses, sea states, accelerations of the net assembly, and displacements of the location buoy were selected as the input variables. The backpropagation learning algorithm was employed in training to maximize damage-detection performance. The output of the ANN model was the identification of the particular location of the damaged net. In testing, big data, which were not used in training, were utilized. Well-trained ANN models detected damage to the net even at sea states that were not included in training with high accuracy.

Download Full-text

Evaluation of soft computing and regression-based techniques for the estimation of evaporation

Journal of Water and Climate Change ◽

10.2166/wcc.2019.101 ◽

2019 ◽

Cited By ~ 1

Author(s):

Aparajita Singh ◽

R. M. Singh ◽

A. R. Senthil Kumar ◽

Ashish Kumar ◽

Subodh Hanwat ◽

...

Keyword(s):

Learning Algorithm ◽

Activation Function ◽

Regional Level ◽

Maximum Temperature ◽

Efficiency Coefficient ◽

Ann Model ◽

Effective Parameters ◽

Efficient Planning ◽

Artificial Neural Network Ann ◽

Limited Input Data

Abstract The estimation of evaporation in the field as well as the regional level is required for the efficient planning and management of water resources. In the present study, artificial neural network (ANN) and multiple linear regression (MLR)-based models were developed to estimate the pan evaporation on the basis of one day-lagged rainfall (Pt−1), one day-lagged relative humidity (RHt−1), current day maximum temperature (Tmax) and minimum temperature (Tmin). These were selected as the most effective parameters on the basis of cross-correlation. The performance of models was evaluated using correlation coefficient (r), root-mean-square error (RMSE) and Nash–Sutcliffe efficiency (coefficient of efficiency, CE) during calibration and validation periods. Based on the comparison, the ANN model (4-9-1), with sigmoid as activation function and Levenberg–Marquardt as a learning algorithm, was selected as the best performing model among all ANN models. The values of r, CE and RMSE for training and validation periods were found as 0.885, 0.785 and 1.00 mm/day and 0.889, 0.782 and 1.01 mm/day, respectively, through the ANN model (4-9-1). The values of r, CE and RMSE for training and validation periods were found as 0.835, 0.698 and 1.19 mm/day and 0.866, 0.750 and 1.15 mm/day, respectively, through the selected MLR model. Based on the sensitivity analysis, RHt−1 is selected as the most effective parameter followed by Pt−1, Tmax and Tmin. The developed model can be utilized as an alternative for the estimation of the evaporation at the regional level with limited input data.

Download Full-text

Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

High Energy ◽

Ann Model ◽

Artificial Neural ◽

Rotary Kilns ◽

Input Variables ◽

Artificial Neural Network Ann ◽

Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

Download Full-text

Modeling the concentration of carbonyl of ethylene propylene diene monomer during the thermal aging using artificial neural network

Algerian Journal of Renewable Energy and Sustainable Development ◽

10.46657/ajresd.2021.3.1.5 ◽

2021 ◽

Vol 03 (01) ◽

pp. 45-52

Author(s):

Hadjira Maouz ◽

◽

Asma Adda ◽

Salah Hanini ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Thermal Aging ◽

Training Data ◽

Ethylene Propylene Diene Monomer ◽

Ann Model ◽

Ethylene Propylene ◽

Artificial Neural ◽

Input Variables ◽

Artificial Neural Network Ann

The concentration of carbonyl is one of the most important properties contributing to the detection of the thermal aging of polymer ethylene propylene diene monomer (EPDM). In this publication, an artificial neural network (ANN) model was developed to predict concentration of carbenyl during the thermal aging of EPDM using a database consisting of seven input variables. The best fitting training data was obtained with the architecture of (7 inputs neurons, 10 hidden neurons and 1 output neuron). A Levenberg Marquardt learning (LM) algorithm, hyperbolic tangent transfer function were used at the hidden and output layer respectively. The optimal ANN was obtained with a high correlation coefficient R= 0.995 and a very low root mean square error RMSE = 0.0148 mol/l during the generalization phase. The comparison between the experimental and calculated results show that the ANN model is able of predicted the concentration of carbonyl during the thermal aging of ethylene propylene diene monomer

Download Full-text

Net surface radiation retrieval using Earth Observation Satellite data and machine learning algorithm

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-8-9-2014 ◽

2014 ◽

Vol II-8 ◽

pp. 9-12 ◽

Cited By ~ 5

Author(s):

D. V. Mahalakshmi ◽

A. Paul ◽

D. Dutta ◽

M. M. Ali ◽

C. S. Jha ◽

...

Keyword(s):

Land Surface ◽

Learning Algorithm ◽

Dew Point ◽

Surface Radiation ◽

Ann Model ◽

Agricultural Water Management ◽

Atmospheric Profile ◽

Artificial Neural Network Ann ◽

Terra Modis ◽

Independent Parameters

We present a method to estimate net surface radiation (NSR) from Terra MODIS data using Artificial Neural Network (ANN) technique. For this purpose, we trained the ANN model using MODIS atmospheric profile product of air temperature, dew point temperature, solar zenith angle and land surface temperature from Terra as independent parameters and the net surface radiation from eddy flux tower measurements at Bonnie camp location of Sundarban region as the dependent variable. The NSR is estimated with a root mean square accuracy of 64 w/m<sup>2</sup> and the square of the correlation coefficient (R<sup>2</sup>) is 0.75 respectively. This technique is extended to estimate NSR over the entire Sundarban area and has a potential for climate and agricultural water management studies.

Download Full-text

Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

Journal of Advanced Industrial Technology and Application ◽

10.30880/jaita.2021.02.01.003 ◽

2021 ◽

Vol 02 (01) ◽

Author(s):

Natasha Munirah Mohd Fahmi ◽

◽

Nor Aira Zambri ◽

Norhafiz Salim ◽

Sim Sy Yi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Climatic Factors ◽

Activation Function ◽

Solar Panels ◽

Ann Model ◽

Power Characteristics ◽

Step Procedure ◽

Artificial Neural ◽

Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

Download Full-text

An AI-Based Model for Predicting Instananeous Cutting Force in End Milling

Manufacturing ◽

10.1115/imece2002-33662 ◽

2002 ◽

Author(s):

Hazim El-Mounayri ◽

Vipul Tandon

Keyword(s):

Cutting Force ◽

Frequency Domain ◽

Cutting Forces ◽

End Milling ◽

Depth Of Cut ◽

Feed Speed ◽

Ann Model ◽

Force Signal ◽

Input Variables ◽

Artificial Neural Network Ann

An Artificial Neural Network (ANN) model is developed to accurately predict the instantaneous cutting forces in flat end milling. A unique frequency domain approach is presented and is seen to simulate instantaneous cutting forces reasonably well. A set of eight input variables is chosen to represent the machining conditions and frequency domain parameters of the cutting force signal are generated. Three input parameters are varied, namely Feed, Speed and Depth of Cut. Four output parameters are suggested as a sufficient set to adequately reproduce the instantaneous cutting forces. Exhaustive experimentation is conducted to collect data (consisting of Fx, Fy, and Fz) to train and validate the model.

Download Full-text

Advanced approaches to hot-mix asphalt dynamic modulus prediction

Canadian Journal of Civil Engineering ◽

10.1139/l08-016 ◽

2008 ◽

Vol 35 (7) ◽

pp. 699-707 ◽

Cited By ~ 35

Author(s):

Halil Ceylan ◽

Kasthurirangan Gopalakrishnan ◽

Sunghwan Kim

Keyword(s):

Prediction Model ◽

Dynamic Modulus ◽

Prediction Models ◽

Hot Mix Asphalt ◽

Logical Structure ◽

Ann Model ◽

Model Predictions ◽

Input Variables ◽

Artificial Neural Network Ann ◽

National Cooperative

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.

Download Full-text

Prediction of Locations in Medical Images Using Orthogonal Neural Networks

10.21203/rs.3.rs-649890/v1 ◽

2021 ◽

Author(s):

Jong Soo Kim ◽

Yongil Cho ◽

Tae Ho Lim

Keyword(s):

Neural Network ◽

Diagnostic Performance ◽

Medical Images ◽

Selection Criterion ◽

Activation Function ◽

X Rays ◽

Ann Model ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Image Localization

Abstract An orthogonal neural network (ONN), a new deep-learning structure for medical image localization, is developed and presented in this paper. This method is simple, efficient, and completely different from a convolution neural network (CNN). The diagnostic performance of ONN for detecting the location of pneumothorax in chest X-rays was assessed and compared to that of CNN. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.870, an accuracy of 85.3%, a sensitivity of 75.0%, and a specificity of 86.5% were achieved; the ONN outperformed the CNN. The diagnostic performance of the ONN with a sigmoid activation function for all the nodes obviously outperformed the ONN with the rectified linear unit (RELU) activation function for all the nodes other than the output nodes. In addition, by applying ONN and CNN to predict the location of the glottis in laryngeal images, we achieved accurate and adjacent prediction rates of 70.5% and 20.5%, respectively, with the ONN. The prediction accuracy of the ONN was compared favorably with that of the CNN. Compared to a CNN, an ONN required only approximately 10% of the computations using a CNN trained on images with an input resolution of 256 × 256 pixels. A fully-connected small artificial neural network (ANN), selected by comparing the test results of several dozens of small ANN models, achieved the best location prediction performance on medical images. This study demonstrated that an ONN can be used as a quick selection criterion to compare ANN models for image localization since an ONN performed well compared decently with the selected ANN model.

Download Full-text