scholarly journals COVID-19 Lockdown in Belgrade: Impact on Air Pollution and Evaluation of a Neural Network Model for the Correction of Low-Cost Sensors’ Measurements

2021 ◽  
Vol 11 (22) ◽  
pp. 10563
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Zoran Cica
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Low Cost ◽  
Training Data ◽  
Traffic Density ◽  
The Impact ◽  
Public Monitoring

In this paper, we explore the impact of the COVID-19 lockdown in Serbia on the air pollution levels of CO, NO2 and PM10 alongside the possibility for low-cost sensor usage during this period. In the study, a device with low-cost sensors collocated with a reference public monitoring station in the city of Belgrade is used for the same period of 52 days in 2019 (pre-COVID-19 period), 2020 (COVID-19 lockdown) and 2021 (post-COVID-19 period). Low-cost sensors’ measurements are improved by using a convolutional neural network that applies corrections of the influence of temperature and relative humidity on the low-cost sensors. As a result of this study we have noticed a remarkable decrease in NO2 (primarily related to traffic density), while on the other hand CO and PM10, related to domestic heating sources and heating plants, showed constant or slightly higher levels. The obtained results are in accordance with other published work in this area. The low-cost sensors have shown a satisfactory correlation with the reference CO measurements during the lockdown, while the NO2 and PM10 measurements of 2020 were corrected using a convolutional neural network trained on meteorological and pollutant data from 2019. The results include an improvement of 0.35 for the R2 of NO2 and an improvement of 0.13 for the R2 of PM10, proving that our neural network model trained on data from 2019 can improve the performance of the sensor in the lockdown period in 2020. This means that our neural network model is very robust, as it exhibits good performance even in the case where training data from the prior year (2019) are used in the following year (2020) in very different environment circumstances—a lockdown.

scholarly journals An Air Pollution Prediction Scheme Using Long Short Term Memory Neural Network Model

2021 ◽  
Vol 257 ◽  
pp. 03027
Author(s):  
Jongchol Kim
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Short Term Memory ◽  
Environmental Pollutants ◽  
Training Data ◽  
Nox Emissions ◽  
Prediction Scheme

In order to establish countermeasures for air pollution, it is first necessary to accurately grasp the air pollution state and predict the cause and change trend of the pollution situation. Due to the continuously strengthening regulations on the emissions of environmental pollutants, the forecasting and management of nitrogen oxides (NOx) emissions is receiving a lot of attention from industrial sites. In this study, a model for predicting nitrogen oxide emissions based on artificial intelligence was proposed. The proposed model includes everything from data preprocessing to learning and evaluation of the model, and used a Long ShortTerm Memory (LSTM) neural network model, one of the recurrent neural networks, to predict NOx emissions with time-series characteristics. The optimized LSTM model showed more than 93% NOx emissions prediction accuracy for both the training data and the evaluation data. The model proposed in this study is expected to be applied to the development of a model for predicting the emission of various air pollutants with time-series characteristics.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

scholarly journals Urban Sound Classification Using Convolutional Neural Network Model

2021 ◽  
Vol 1099 (1) ◽  
pp. 012001
Author(s):  
Srishti Garg ◽  
Tanishq Sehga ◽  
Aakriti Jain ◽  
Yash Garg ◽  
Preeti Nagrath ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Sound Classification

scholarly journals Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Ying Yu ◽  
Yirui Wang ◽  
Shangce Gao ◽  
Zheng Tang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rapid Development ◽  
Seasonal Trend ◽  
Moving Averages ◽  
Forecasting Methods ◽  
Tourism Economy ◽  
The Impact ◽  
D Model

With the impact of global internationalization, tourism economy has also been a rapid development. The increasing interest aroused by more advanced forecasting methods leads us to innovate forecasting methods. In this paper, the seasonal trend autoregressive integrated moving averages with dendritic neural network model (SA-D model) is proposed to perform the tourism demand forecasting. First, we use the seasonal trend autoregressive integrated moving averages model (SARIMA model) to exclude the long-term linear trend and then train the residual data by the dendritic neural network model and make a short-term prediction. As the result showed in this paper, the SA-D model can achieve considerably better predictive performances. In order to demonstrate the effectiveness of the SA-D model, we also use the data that other authors used in the other models and compare the results. It also proved that the SA-D model achieved good predictive performances in terms of the normalized mean square error, absolute percentage of error, and correlation coefficient.

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