scholarly journals Tropospheric Ozone Formation Estimation in Urban City, Bangi, Using Artificial Neural Network (ANN)

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fatin Aqilah Binti Abdul Aziz ◽  
Norliza Abd. Rahman ◽  
Jarinah Mohd Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ozone Concentration ◽  
Tropospheric Ozone ◽  
Air Pollutants ◽  
Rapid Development ◽  
Ground Level ◽  
Urban City ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Due to the rapid development of economy and society around the world, the most urban city is experiencing tropospheric ozone or commonly known as ground-level air pollutants. The concentration of air pollutants must be identified as an early precaution step by the local environmental or health agencies. This work aims to apply the artificial neural network (ANN) in estimating the ozone concentration forecast in Bangi. It consists of input variables such as temperature, relative humidity, concentration of nitrogen dioxide, time, UVA and UVB rays obtained from routine monitoring, and data recorded. Ten hidden layer is utilized to obtain the optimized ozone concentration, which is the output layer of the ANN framework. The finding showed that the meteorology condition and emission patterns play an important part in influencing the ozone concentration. However, a single network is sufficient enough to estimate the concentration despite any circumstances. Thus, it can be concluded that ANN is able to give reliable and satisfactory estimations of ozone concentration for the following day.

scholarly journals Prediction of the Pollutants Concentration Using Artificial Neural Network (ANN)

2015 ◽  
Vol 16 (SE) ◽  
pp. 171-180
Author(s):  
Ahmad Mousavian ◽  
Hady Zarei Mahmodabady ◽  
Aboutaleb Ghadami Jadval Ghadam
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Air Pollutants ◽  
Environmental Issues ◽  
Urban Transport ◽  
Regression Methods ◽  
Tangent Function ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Air pollution is one of the most important environmental issues that annual causes to mortality large number of people around the world. So, investigating, measuring, and predicting the concentrations of different pollutants in various areas play an important role in preventing the production of this pollutant sand planning to reduce them by people and relevant authorities. One of the new models that play an important role in measuring and predicting pollution is artificial neural network or regression methods. Therefore, this study is trying to predict air pollution in Yasouj by using artificial neural network in 2014. Because the evidences showed that Yasouj due to uncontrolled growth of industrial and urban transport is subject to various air pollutants such as carbon monoxide and particulate matter. Overall, the results of the assessment and prediction of concentration of pollutants of Yasouj by artificial neural network showed that sigmoid transfer function to the hyperbolic tangent function is more efficient in measuring the concentration of pollutants.  

scholarly journals Competitiveness Evaluation of Tourist Attractions Based on Artificial Neural Network

2020 ◽  
Vol 34 (5) ◽  
pp. 623-630
Author(s):  
Bingna Lou ◽  
Nan Chen ◽  
Lan Ma
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Evaluation Model ◽  
Rapid Development ◽  
Core Layer ◽  
Evaluation Index System ◽  
Tourist Attractions ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Fierce Competition

With the rapid development of tourism, tourists have become more aware of tourism level and quality. This triggers fierce competition between tourist attractions. To promote the core competitiveness of tourist attractions, this paper proposes a new evaluation model for the competitiveness of tourist attractions based on artificial neural network. First, a four-layer evaluation index system (EIS) was constructed for the competitiveness of tourist attractions, including detail elements, basic layer, core layer, and characterization layer. Next, all the evaluation indices were optimized through clustering by improved k-modes algorithm. Finally, a backpropagation neural network (BPNN) was established to evaluate the competitiveness of tourist attractions. Experimental results confirm the effectiveness of the proposed method. The research findings provide a reference for the application of artificial neural network (ANN) in other prediction fields.

scholarly journals An Outlook of Ozone Air Pollution through Comparative Analysis of Artificial Neural Network, Regression, and Sensitivity Models

2018 ◽  
Vol 8 (5) ◽  
pp. 3387-3391
Author(s):  
J. S. Khan ◽  
S. Khoso ◽  
Z. Iqbal ◽  
S. Sohu ◽  
M. A. Keerio
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Ozone Concentration ◽  
Government Agencies ◽  
Ann Model ◽  
Sustainable Environment ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Climate Prediction Center

Air pollution and atmospheric ozone can cause damages to human health and to the environment. This study explores the potential approach of the artificial neural network (ANN) model and compares it with a regression model for predicting ozone concentration using different parameters and functions measured by the Climate Prediction Center of US National Weather Service. In addition, this study has compared the economic viability of ANN and other measuring methods. Results showed that the ANN-based model exhibited better performance. Such model types can be beneficial to government agencies. By predicting ozone concentration government agencies can take preventive measures to avoid significant health effects, protect local populations, and help preserve a sustainable environment.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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