scholarly journals Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 713
Author(s):  
Osman Taylan ◽  
Abdulaziz S. Alkabaa ◽  
Mohammed Alamoudi ◽  
Abdulrahman Basahel ◽  
Mohammed Balubaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Urban Areas ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Nonlinear Least Squares ◽  
Optimization Method ◽  
Total Effect ◽  
Learning Approaches ◽  
Jeddah City

Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision-making. Several important pollutants, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have important effects on air quality. Air quality should be recorded and measured based on the total effect of pollutants that are collectively prescribed by a numerical value. In Canada, the Air Quality Health Index (AQHI) is used which is one numerical value based on the total effect of some concentrations. Therefore, evolution is required to consider the complex, ill-defined air pollutants, hence several naive and noble approaches are used to study AQHI. In this study, three approaches such as hybrid data-driven ANN, nonlinear autoregressive with external (exogenous) input (NARX) with a neural network, and adaptive neuro-fuzzy inference (ANFIS) approaches are used for estimating the air quality in an urban area (Jeddah city—industrial zone) for public health concerns. Over three years, 1771 data were collected for pollutants from 1 June 2016 until 30 September 2019. In this study, the Levenberg-Marquardt (LM) approach was employed as an optimization method for ANNs to solve the nonlinear least-squares problems. The NARX employed has a two-layer feed-forward ANN. On the other hand, the back-propagation multi-layer perceptron (BPMLP) algorithm was used with the steepest descent approach to reduce the root mean square error (RMSE). The RMSEs were 4.42, 0.0578, and 5.64 for ANN, NARX, and ANFIS, respectively. Essentially, all RMSEs are very small. The outcomes of approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of artificial intelligent techniques, the public’s early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed notable performance of NARX with a neural network, ANN, and ANFIS-based AQHI model for high dimensional data assessment.

scholarly journals Air Quality Modeling for Sustainable Clean Environment Using ANFIS and Machine Learning Approaches

2021 ◽  
Author(s):  
Osman Taylan ◽  
Abdulaziz Alkabaa ◽  
Mohammed Alamoudi ◽  
Abdulrahman Basahel ◽  
Mohammad Balubaid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Urban Areas ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Nonlinear Least Squares ◽  
Optimization Method ◽  
Learning Approaches ◽  
Quality Models ◽  
Neuro Fuzzy

Abstract Air quality monitoring and assessment are essential issues for sustainable environmental protection. The monitoring process is composed of data collection, evaluation, and decision making. Several important pollution factors, such as SO2, CO, PM10, O3, NOx, H2S, location, and many others, have detrimental effects on air quality. Air quality cannot be precisely recorded and measured due to the total effect of pollutants that usually cannot be collectively prescribed by a numerical value. Therefore, evolution is required to take into account the complex, poorly defined air quality problems in which several naive and noble modeling approaches are used to evaluate and solve. In this study, hybrid data-driven machine learning, and neuro-fuzzy methods are integrated for estimating the air quality in the urban area for public health concerns. 1771 data are collected during three years for each pollution factor, starting from June 1, 2016, till September 30, 2019. The Back-Propagation Multi-Layer Perceptron (BPMLP) algorithm was employed with the steepest descent approach to reduce the mean square error for training the algorithm of the neuro-fuzzy model. Levenberg-Marquardt (LM) approach was also employed as an optimization method with Artificial neural networks (ANNs) for solving nonlinear least-squares problems in this study. These approaches were evaluated by fuzzy quality charts and compared statistically with the US-EPA air quality standards. Due to the effectiveness and robustness of soft computing intelligent models, the public's early warning will be possible for avoiding the harmful effects of pollution inside the urban areas, which may reduce respiratory and cardiovascular mortalities. Consequently, the stability of air quality models was correlated with the absolute air quality index. The findings showed remarkable performance of ANFIS and ANN-based Air Quality models for High dimensional data assessment.

Research of the 3-DOF Helicopter System Based on Adaptive Inverse Control

2013 ◽  
Vol 389 ◽  
pp. 623-631 ◽  
Author(s):  
Xiu Yan Wang ◽  
Ying Wang ◽  
Zong Shuai Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Fuzzy Inference ◽  
Reference Model ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Actual System ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Network Connection

For the flight control problem occurred in 3-DOF Helicopter System, reference adaptive inverse control scheme based on Fuzzy Neural Network model is designed. Firstly, fuzzy inference process of identifier and controller is achieved by using the network structure. Meanwhile, the neural network connection weights are used to express parameters of fuzzy inference. Then, back-propagation algorithm is adopted to amend the network connection weights in order to automatically identify the fuzzy model and adjust its membership functions and parameters, so that the actual system output of adaptive inverse controller control which is adjusted can track the reference model output. Finally, the simulation result of 3-DOF Helicopter System based on the scheme shows that the method is effective and feasible.

scholarly journals OPTIMIZATION RAINFALL-RUNOFF MODELING FOR CIUJUNG RIVER USING BACK PROPAGATION METHOD

SINERGI ◽  
2018 ◽  
Vol 22 (3) ◽  
pp. 193
Author(s):  
Ika Sari Damayanthi Sebayang ◽  
Agus Suroso ◽  
Alnis Gustin Laoli
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Urban Areas ◽  
Back Propagation ◽  
Water Volume ◽  
Rainfall Runoff ◽  
Back Propagation Algorithm ◽  
Runoff Modeling ◽  
Artificial Neural ◽  
The Relationship

The rainfall-runoff model is required to ascertain the relationship between rainfall and runoff. Hydrologists are often confronted with problems of prediction and estimation of runoff using the rainfall date. In actual fact the relationship of rainfall-runoff is known to be highly non-linear and complex. The spatial and temporal precipitation patterns and the variability of watershed characteristics create a more complex hydrologic phenomenon. Runoff is part of the rain water that enters and flows and enters the river body. Rainfall-runoff modeling in this study using Artificial Neural Network, back propagation method and sigmoid binary activation function. This model is used to simulate single or long-term continuous events, water volume, making it very appropriate for urban areas. Back propagation is an inherited learning algorithm and is commonly used by perceptron with multiple layers to change the weights associated with neurons in the hidden layer. Back propagation algorithm uses output error to change the values of its weight in the backward direction. The location of the review is the Ciujung River Basin (DAS), the data used are rainfall and debit data of Ciujung River from 2011-2017. Based on training and simulation results, obtained R2 value: 2012 = 0,85102; 2013 = 0,78661; 2014 = 0,81188; 2015 = 0,77902; 2016 = 0,7279. on model 2 = 0,8724. On model 3 R2:  January = 0,96937; February = 0,92984; March = 0,90666; April = 0,92566; May = 0,9128; June = 0,87975; July= 0,85292; August = 0,95943; September = 0,88229; October = 0,90537; November = 0,93522; December = 0,9111. with MSE (Mean Squared Error) of 0,0018479. The closer value of MSE to 0 and the value of R2 close to 1 then the better designed artificial neural network. If the data used for training more, the artificial neural network will produce a larger R2 value.

scholarly journals Machine Learning Approaches for Predicting Geometric and Mechanical Characteristics for Single P420 Laser Beads Clad onto an AISI 1018 Substrate

2021 ◽  
Author(s):  
Bita Mohajernia ◽  
Seyedeh Elnaz Mirazimzadeh ◽  
Alireza Pasha ◽  
Ruth Jill Urbanic
Keyword(s):  
Mechanical Properties ◽  
Cross Sections ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Bead Geometry ◽  
Learning Approaches ◽  
Inference System ◽  
Geometrical Properties ◽  
Feed Forward Back Propagation ◽  
Discrete Values

Abstract For effective bead deposition based additive manufacturing (AM) processes such as directed energy deposition, the final mechanical and physical properties should be predicted in tandem with the bead geometry characteristics. Experimental approaches to investigate the final geometry and the mechanical properties are costly, and simulation solutions are time-consuming. Alternative artificial intelligent (AI) systems are explored as they are a powerful approach to predict such properties. In the present study, the geometrical properties, as well as the mechanical properties (residual stress and hardness) for single bead clads are investigated. Experimental data is used to calibrate multi-physics finite element models, and both data sets are used to seed the AI models. The adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward back-propagation artificial neural network (ANN) system are utilized to explore their effectiveness in the 1D (discrete values), 2D (cross sections), and 3D (complete bead) domains. The prediction results are evaluated using the mean relative error measure. The ANFIS predictions are more precise than those from the ANN for the 1D and 2D domains, but the ANN had less error for the 3D scenario. These models are capable of predicting the geometrical and the mechanical properties values very well, including capturing the mechanical properties in transient regions; however, this research should be extended for multi- bead scenarios before a conclusive ‘best approach’ strategy can be determined.

scholarly journals ANFIS AND NEURAL NETWORK BASED FACIAL EXPRESSION RECOGNITION USING CURVELET FEATURES

2014 ◽  
pp. 255-261
Author(s):  
S.P. Khandait ◽  
R.C. Thool ◽  
P.D Khandait
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Curvelet Transform ◽  
Back Propagation Neural Network ◽  
Expression Recognition ◽  
Inference System ◽  
Promising Tool

Curvelet transform is a promising tool for multi-resolution analysis on images. This paper explains a new approach for facial expression recognition based on curvelet features extracted using curvelet transform. Curvelet transform is applied on the database images and curvelet coefficients are obtained for selected scale for image analysis. Facial curvelet features are compressed using singular value decomposition (SVD) approach. Back propagation neural network (BPNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are used as classifiers for classifying expressions into one of the seven categories like angry, disgust, fear, happy, neutral, sad and surprise. Experimentation is carried out on JAFFE database. The experimental results show that the novel approach is a better option for extracting feature values and classifying facial expressions.

scholarly journals Prediction of Pullout Behavior of Belled Piles through Various Machine Learning Modelling Techniques

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3678 ◽  
Author(s):  
Dieu Tien Bui ◽  
Hossein Moayedi ◽  
Mu’azu Mohammed Abdullahi ◽  
Ahmad Safuan A Rashid ◽  
Hoang Nguyen
Keyword(s):  
Neural Network ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Least Square ◽  
General Regression Neural Network ◽  
Training Dataset ◽  
Coefficient Of Determination ◽  
Inference System ◽  
Testing Dataset

The main goal of this study is to estimate the pullout forces by developing various modelling technique like feedforward neural network (FFNN), radial basis functions neural networks (RBNN), general regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS). A hybrid learning algorithm, including a back-propagation and least square estimation, is utilized to train ANFIS in MATLAB (software). Accordingly, 432 samples have been applied, through which 300 samples have been considered as training dataset with 132 ones for testing dataset. All results have been analyzed by ANFIS, in which the reliability has been confirmed through the comparing of the results. Consequently, regarding FFNN, RBNN, GRNN, and ANFIS, statistical indexes of coefficient of determination (R2), variance account for (VAF) and root mean square error (RMSE) in the values of (0.957, 0.968, 0.939, 0.902, 0.998), (95.677, 96.814, 93.884, 90.131, 97.442) and (2.176, 1.608, 3.001, 4.39, 0.058) have been achieved for training datasets and the values of (0.951, 0.913, 0.729, 0.685 and 0.995), (95.04, 91.13, 72.745, 66.228, 96.247) and (2.433, 4.032, 8.005, 10.188 and 1.252) are for testing datasets indicating a satisfied reliability of ANFIS in estimating of pullout behavior of belled piles.

A Hybrid Learning Strategy for Real Hardware of Swing-Up Pendulum

2007 ◽  
Vol 11 (8) ◽  
pp. 972-978 ◽  
Author(s):  
Shingo Nakamura ◽  
◽  
Ryo Saegusa ◽  
Shuji Hashimoto
Keyword(s):  
Neural Network ◽  
Learning Strategy ◽  
Back Propagation ◽  
Optimal Controller ◽  
Learning Approaches ◽  
Learning Method ◽  
Data Sampling ◽  
Target Task ◽  
The Real ◽  
Real Hardware

Generally, the bottom-up learning approaches, such as neural-network, to obtain the optimal controller of target task for mechanical system face a problem including huge number of trials, which require much time and give stress against the hardware. To avoid such problems, a simulator is often built and performed with a learning method. However, there are also problems that how simulator is constructed and how accurate it performs. In this paper, we are considering a construction of simulator directly from the real hardware. Afterward a constructed simulator is used for learning target task and the obtained optimal controller is applied to the real hardware. As an example, we picked up the pendulum swing-up task which was a typical nonlinear control problem. The construction of a simulator is performed by back-propagation method with neural-network and the optimal controller is obtained by reinforcement learning method. Both processes are implemented without using the real hardware after the data sampling, therefore, load against the hardware gets sufficiently smaller, and the objective controller can be obtained faster than using only the hardware. And we consider that our proposed method can be a basic learning strategy to obtain the optimal controller of mechanical systems.

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