The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Shams, Seyedeh Reyhaneh; Jahani, Ali; Kalantary, Saba; Moeinaddini, Mazaher; Khorasani, Nematollah

doi:10.1016/j.uclim.2021.100837

Cited by 100 publications

(31 citation statements)

References 42 publications

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“…In this paper, input variables include landform, phenological stages, and soil characteristics, and the output variable is the content of hyperforin. The MLP model by weighting variables and summarizing them produced the most accurate output in previous study by Shams et al (2020Shams et al ( , 2021 and Pourmohammad et al (2020). At first, 60% of samples put in use in the training process.…”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 92%

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A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

Saffariha

Jahani²,

Jahani

2021

Plant Direct

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Hyperforin, a major bioactive constituent of Hypericum concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in Hypericum perforatum based on different ecological and phenological conditions. We employed artificial intelligence modeling techniques including multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) to examine the factors critical in predicting hyperforin content. We found that the MLP model (R2 = .9) is the most suitable and precise model compared with RBF (R2 = .81) and SVM (R2 = .74) in predicting hyperforin in H. perforatum based on ecological conditions, plant growth, and soil features. Moreover, phenological stages, organic carbon, altitude, and total N are detected in sensitivity analysis as the main factors that have a considerable impact on hyperforin content. We also report that the developed graphical user interface would be adaptable for key stakeholders including producers, manufacturers, analytical laboratory managers, and pharmacognosists.

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Section: Multilayer Perceptron Neural Networkmentioning

confidence: 92%

“…Neurons are the main elements used in the MLP model (Shams et al, 2020(Shams et al, , 2021. To get an accurate model, hidden layers, transfer functions, and neurons must be carefully analyzed.…”

Section: Multilayer Perceptron Neural Networkmentioning

confidence: 99%

A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

Saffariha

Jahani²,

Jahani

2021

Plant Direct

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“…Regression models are pure statistical models based on the empirical relations among different variables and are very useful in Prediction and Data Description. Multiple Linear Regression (MLR) assumes a linear relationship between the independent https://www.indjst.org/ and dependent variables (8) , and it is used to examine the relationships between the variables. Once each of the independent factors has been determined to predict the dependent variable, the information on the multiple variables can be used to create an accurate prediction on the level of effect they have on the outcome variable.…”

Section: Multiple Linear Regression Modelmentioning

confidence: 99%

“…The authors (7) present an LSTM model for traffic flow prediction. ANN model and Multiple Linear Regression model adopted by (8,9) for forecasting surface ozone density, CO, NO 2 , and SO 2 concentration using the historical concentration of data. The authors (10) discussed the implementation of LSTM and ARIMA models to forecast the Air pollutants such as CO and NH 3 .…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Total Column Ozone using Regression Analysis and LSTM-RNN Machine Learning Approach

Anu¹,

Elampari²

2022

IJST

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Objectives: Atmospheric Ozone plays an important role in global climate change, human health and environmental conditions. Accurate and timely prediction of variation in Total Column Ozone (TCO) concentration is very important for both climatology and environment. In this context, the present study aims to utilize the advancement of Artificial Intelligence in Machine learning and a conventional method to develop models for the prediction of TCO concentration using historical time series data over a tropical region, India. Methods: Long Short-Term Memory (LSTM) deep learning networks are very useful in classifying, processing and making predictions based on time series data. In this work, Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM) models are developed for forecasting TCO over a tropical region. The Statistical Parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to analyze and evaluate the performance of the proposed models. Findings: In the present study the TCO concentration varied between 217.4 DU and 288.6 DU. The performances of MLR and LSTM model were compared.From the results, it is evident that the values of RMSE, MAE and MAPE of the MLR model are 3.807924, 2.93766, 1.15 respectively whereas the values for LSTM model are 3.074492, 2.574, 1.01 respectively. LSTM was found to be the accurate model for the study data set and the predicted TCO concentration has a very good correlation with the actual observations and it does not rely on the meteorological parameters. Novelty: The LSTM models are usually employed in the field of machine translation, speech recognition and for the prediction of traffic levels, stock levels and air pollutants. This work utilizes the fast numerical machine learning computing library Tensorflow and a high level neural network library Keras that runs on top of Tensorflow to develop a LSTM network model for the prediction of the concentration levels of TCO.

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“…Combined with this paper's exploration of the meaning of these pollutants and their impact on air quality, in this research, we finally select these six pollutants as indicators to characterize air quality and its fluctuations. These six indicators all affect the regional air quality to varying degrees, and the specific indicators include CO [21], O 3 [22], NO 2 [23], SO 2 [24], PM 10 [23] and PM 2.5 [25]. The meanings of them are shown in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

Zhang

Chen

et al. 2022

IJERPH

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Exploring the spatial and temporal distribution characteristics of air quality has become an important topic for the harmonious development of human and nature. Based on the hourly data of CO, O3, NO2, SO2, PM2.5 and PM10 of 1427 air quality monitoring stations in China in 2016, this paper calculated the annual mean and annual standard deviation of six air quality indicators at each station to obtain 12 variables. Self-Organizing Maps (SOM) and K-means clustering algorithms were carried out based on MATLAB and SPSS Statistics, respectively. Kriging interpolation was used to get the clustering distribution of air quality and fluctuation in China, and Principal Component Analysis (PCA) was used to analyze the main factors affecting the clustering results. The results show that: (1) Most areas in China are low-value regions, while the high-value region is the smallest and more concentrated. Air quality in northern China is worse, and the annual fluctuations of the indicators are more dramatic. (2) Compared with AQI, AQFI has a strong indication significance for the comprehensive situation of air quality and its fluctuation. (3) The spatial distribution of SOM clustering results is more discriminative, while K-means clustering results have a large proportion of low-mean regions. (4) PM2.5, PM10 and CO are the main pollutants affecting air quality and fluctuation, followed by SO2, NO2 and O3.

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The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration

Cited by 100 publications

References 42 publications

A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

Forecasting of Total Column Ozone using Regression Analysis and LSTM-RNN Machine Learning Approach

Study on Air Quality and Its Annual Fluctuation in China Based on Cluster Analysis

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