A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection

Masmoudi, Sahar; Elghazel, Haytham; Taieb, Dalila; Yazar, Orhan; Kallel, Amjad

doi:10.1016/j.scitotenv.2020.136991

Cited by 81 publications

(36 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The accuracy of model performance depends on many factors, such as ML algorithms, spatial characteristics, prediction targets, temporal resolution, etc. Several authors have mentioned the structural limitations of algorithms, such as the tendency to overfit, complexity, difficulty with interpretation, and time-consuming [36][37][38]. Regarding the prediction target, depending on which pollutant is the prediction target the accuracy may vary since the chemical structure of the pollutants is different.…”

Section: Resultsmentioning

confidence: 99%

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

View full text Add to dashboard Cite

Air pollution and its consequences are negatively impacting on the world population and the environment, which converts the monitoring and forecasting air quality techniques as essential tools to combat this problem. To predict air quality with maximum accuracy, along with the implemented models and the quantity of the data, it is crucial also to consider the dataset types. This study selected a set of research works in the field of air quality prediction and is concentrated on the exploration of the datasets utilised in them. The most significant findings of this research work are: (1) meteorological datasets were used in 94.6% of the papers leaving behind the rest of the datasets with a big difference, which is complemented with others, such as temporal data, spatial data, and so on; (2) the usage of various datasets combinations has been commenced since 2009; and (3) the utilisation of open data have been started since 2012, 32.3% of the studies used open data, and 63.4% of the studies did not provide the data.

show abstract

Section: Resultsmentioning

confidence: 99%

Features Exploration from Datasets Vision in Air Quality Prediction Domain

2021

View full text Add to dashboard Cite

show abstract

“…In addition, predictor variables can also be added using precursor concentration data including CH4, CO, NMHC, NO, NO2, THC (Wasi'ah and Driejana 2017) which can add information in predictions. Then, the selection of variables using RFE can be compared with new methods such as Ensemble of Regressor Chains-guided Feature Ranking (Masmoudi et al 2020) to find out whether RFE's performance is the same or inferior to that method. In addition, HHO can also be compared with new metaheuristic algorithms such as the Equilibrium Optimizer (Faramarzi et al 2020), Archimedes Optimization (Hashim et al 2021), and Water Streams Optimization (Majani and Nasri 2021).…”

Section: Discussionmentioning

confidence: 99%

Using Harris hawk optimization towards support vector regression to ozone prediction

Kurniawan¹,

Setiawan

Caraka

et al. 2022

Stoch Environ Res Risk Assess

View full text Add to dashboard Cite

As an area experiencing air pollution, especially ozone concentrations that often exceed the threshold or are unhealthy, JABODETABEK (Jakarta, Bogor, Depok, Tangerang, and Bekasi) seeks to prevent and control pollution as well as restore air quality. Therefore, this study aims to build a predictive model of ozone concentration using Harris hawks optimizationsupport vector regression (HHO-SVR) in 14 sub-districts in JABODETABEK. This goal is achieved by collecting data on ozone concentration as a response variable and meteorological factors as predictor variables from the website that provides the data. Other predictor variables such as time and significant lag detected with partial autocorrelation function of ozone concentration were also used. Then the variables will be selected using the recursive feature elimination-support vector regression (RFE-SVR) to obtain a significant predictor variable that affects the ozone concentration. After that, the prediction model will be built using the HHO-SVR method, support vector regression (SVR) whose parameter values are optimized with the Harris hawks optimization (HHO) algorithm. When the model has been formed, several evaluation metrics used to determine the best model include mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), Coefficient of Determination (R 2 ), Variance Ratio (VR), and Diebold-Mariano test. The results of this study indicate that lag 1, lag 2, air temperature, humidity, and UV index are significant predictor variables of the RFE-SVR results for most sub-districts. In general, the HHO process takes longer than other metaheuristic algorithms. On average, 7 of the 14 sub-districts using the HHO-SVR model yielded the best predictions with MAE below 10, RMSE and MAPE below 20, R 2 around 0.97, and VR around 0.98. Then, the results of the Diebold-Mariano test also show that the accuracy of the prediction results and the stability of the performance of the HHO-SVR model is better, especially for the Ciputat and South Bekasi sub-districts. This shows that the two sub-districts are very suitable to use HHO-SVR in predicting ozone concentrations.

show abstract

“…Some researchers applied neural networks or ANFIS technique (e.g., Bakhshandeh Amnieh et al 2010; Armaghani et al 2014;Kocaslan et al 2017, Ozer et al 2019, Ozer et al 2020. Hasanipanah et al (2015), Dindarloo (2015), and Masmoudi et al (2020) practiced support vector machines and machine learning methodology to investigate environmental issues. In addition, genetic algorithms have been widely used to predict blast-induced ground vibrations (e.g., Faradonbeh et al 2016;Singh et al 2016;Azimi et al 2019;Tian et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

Hudaverdi

Akyildiz

2021

Arab J Geosci

View full text Add to dashboard Cite

This research focuses on blast-induced structural damage and human response to vibrations. Frequency is one of the important parameters that affect damage probability and human perception. The first and primary target of the study is to predict ground vibration and frequency simultaneously. A feed-forward back propagation neural network was created for modeling. Three operational parameters and vibration measurement distance were used as input parameters. The developed model was compared to the regression equations. Different error measures were considered to perform a vigorous model validation. Ten different blasting level charts were used to estimate structural damage probability. The regulations from North America, Europe, and Southern Hemisphere were examined and compared. Variation in the vibration regulations was also discussed. Some suggestions were put forward for the development of an international regulation. Two practical charts were created to predict human response to vibrations. A specific graph, which was proposed for pile driving induced vibrations, was considered for evaluation of blast vibrations. The developed soft computing framework can forecast probability of structural damage and human perception of vibrations effectively.

show abstract

A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection

Cited by 81 publications

References 17 publications

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Features Exploration from Datasets Vision in Air Quality Prediction Domain

Using Harris hawk optimization towards support vector regression to ozone prediction

Prediction and evaluation of blast-induced ground vibrations for structural damage and human response

Contact Info

Product

Resources

About