1
|
Mohammadi F, Teiri H, Hajizadeh Y, Abdolahnejad A, Ebrahimi A. Prediction of atmospheric PM 2.5 level by machine learning techniques in Isfahan, Iran. Sci Rep 2024; 14:2109. [PMID: 38267539 PMCID: PMC10808097 DOI: 10.1038/s41598-024-52617-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/21/2024] [Indexed: 01/26/2024] Open
Abstract
With increasing levels of air pollution, air quality prediction has attracted more attention. Mathematical models are being developed by researchers to achieve precise predictions. Monitoring and prediction of atmospheric PM2.5 levels, as a predominant pollutant, is essential in emission mitigation programs. In this study, meteorological datasets from 9 years in Isfahan city, a large metropolis of Iran, were applied to predict the PM2.5 levels, using four machine learning algorithms including Artificial Neural |Networks (ANNs), K-Nearest-Neighbors (KNN), Support Vector |Machines (SVMs) and ensembles of classification trees Random Forest (RF). The data from 7 air quality monitoring stations located in Isfahan City were taken into consideration. The Confusion Matrix and Cross-Entropy Loss were used to analyze the performance of classification models. Several parameters, including sensitivity, specificity, accuracy, F1 score, precision, and the area under the curve (AUC), are computed to assess model performance. Finally, by introducing the predicted data for 2020 into ArcGIS software and using the IDW (Inverse Distance Weighting) method, interpolation was conducted for the area of Isfahan city and the pollution map was illustrated for each month of the year. The results showed that, based on the accuracy percentage, the ANN model has a better performance (90.1%) in predicting PM2.5 grades compared to the other models for the applied meteorological dataset, followed by RF (86.1%), SVM (84.6%) and KNN (82.2%) models, respectively. Therefore, ANN modelling provides a feasible procedure for the managerial planning of air pollution control.
Collapse
Affiliation(s)
- Farzaneh Mohammadi
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hakimeh Teiri
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Yaghoub Hajizadeh
- Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Diseases, Isfahan University of Medical Sciences, Hezar Jerib Street, Isfahan, 8174673461, Iran.
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Ali Abdolahnejad
- Department of Environmental Health Engineering, School of Public Health, Maragheh University of Medical Sciences, Maragheh, Iran
| | - Afshin Ebrahimi
- Environment Research Center, Research Institute for Primordial Prevention of Non-Communicable Diseases, Isfahan University of Medical Sciences, Hezar Jerib Street, Isfahan, 8174673461, Iran
- Department of Environmental Health Engineering, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|
2
|
Mohammadi F, Yavari Z, Nikoo MR, Al-Nuaimi A, Karimi H. Machine learning model optimization for removal of steroid hormones from wastewater. CHEMOSPHERE 2023; 343:140209. [PMID: 37741365 DOI: 10.1016/j.chemosphere.2023.140209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/29/2023] [Accepted: 09/16/2023] [Indexed: 09/25/2023]
Abstract
In the past few decades, there has been a significant focus on detecting steroid hormones in aquatic environments due to their influence on the endocrine system. Most compounds of these pollutants are the natural steroidal estrogens, i.e., estrone (E1), 17β-Estradiol (E2), and the synthetic estrogen 17α-Ethinylestradiol (EE2). The Moving-Bed Biofilm Reactor (MBBR) technique is appropriate for eliminating steroid hormones. This study centers on creating a model to estimate the effectiveness of the MBBR system regarding its ability to eliminate E1, E2, and EE2. The results were modeled with artificial neural networks (ANNs). The Particle Warm Optimization (PSO) and Levenberg Marquardt (LM) algorithms were selected for network training. The models incorporated five input parameters, encompassing the COD loading rate, initial levels of E1, E2, and EE2 steroid hormones, and Hydraulic Retention Time (HRT). The optimum removal conditions (three steroid hormones and COD) were determined using the optimized ANN based on both PSO and LM algorithms. The optimal transfer functions for the hidden and output layers were identified as tan-sigmoid and linear, respectively. The best ANN structures (Neurons in input, hidden, and output layers) and correlation coefficients (R) were 5:9:4, with R = 0.9978, and 5:10:4, with R = 0.9982 for the trained networks with LM and PSO algorithms, respectively. Eventually, the input parameters' importance was ranked using sensitivity analysis (SA) through Pearson correlation and developed ANNs.
Collapse
Affiliation(s)
- Farzaneh Mohammadi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Zeinab Yavari
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Mohammad Reza Nikoo
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Ali Al-Nuaimi
- Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
| | - Hossein Karimi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
| |
Collapse
|
3
|
Sensitivity Analysis with the Monte Carlo Method and Prediction of Atenolol Removal Using Modified Multiwalled Carbon Nanotubes Based on the Response Surface Method: Isotherm and Kinetics Studies. INTERNATIONAL JOURNAL OF CHEMICAL ENGINEERING 2022. [DOI: 10.1155/2022/4613023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Atenolol (ATN) is a β-blocker drug extensively used to treat arrhythmias and high blood pressure. Because the human body cannot metabolize it completely, this drug has been commonly found in many environmental matrices. In the present study, the response surface method (RSM) was used for adsorption prediction of ATN on modified multiwalled carbon nanotubes (M-MWCNTs) by NaOCl and ultrasonic. The sensitivity analysis was done by the Monte Carlo method. Sensitivity analysis was performed to determine the effective parameter by the Monte Carlo simulator. Statistical analysis of variance (ANOVA) was performed by using the nonlinear second-order model of RSM. The influential parameters including contact time (min), adsorbent dosage (g/L), pH, and the initial concentration (mg/L) of ATN were investigated, and optimal conditions were determined. Kinetic of ATN adsorption on M-MWCNTs was evaluated using pseudo-first, pseudo-second-order, and intraparticle diffusion models. Equilibrium isotherms for this system were analyzed by the ISOFIT software. As per our result, optimum conditions in the adsorption experiments were pH 7, 60 min of contact time, 0.5 mg/L ATN initial concentration, and 150 mg/L adsorbent dose. In terms of ATN removal efficiency, coefficients of R2 and adjusted R2 were 0.999 and 0.998, respectively. Sensitivity analysis also showed that contact time has the greatest effect on increasing the removal of ATN. Pseudo-first-order (R2 value of 0.99) was the best kinetic model for the adsorption of ATN, and for isotherm, BET (AICC value of 3.27) was the best model that fit the experimental data. According to the obtained results from sensitive analysis, time was the most important parameter, and after that, the adsorbent dose and pH affect positively on ATN removal efficiency. It can be concluded that the modified multiwalled carbon nanotubes can be applied as one of the best adsorbents to remove ATN from the aqueous solution.
Collapse
|
4
|
Mohammadi F, Pourzamani H, Karimi H, Mohammadi M, Mohammadi M, Ardalan N, Khoshravesh R, Pooresmaeil H, Shahabi S, Sabahi M, Sadat Miryonesi F, Najafi M, Yavari Z, Mohammadi F, Teiri H, Jannati M. Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran. Biomed J 2021; 44:304-316. [PMID: 34127421 PMCID: PMC7905378 DOI: 10.1016/j.bj.2021.02.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 02/11/2021] [Accepted: 02/17/2021] [Indexed: 01/08/2023] Open
Abstract
Background COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19. Methods A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19. Results In 750 confirmed patients, Common symptoms were: fever (%) >37.5 °C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model. Conclusion The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection.
Collapse
Affiliation(s)
- Farzaneh Mohammadi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran; Environment Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Hamidreza Pourzamani
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran; Environment Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Hossein Karimi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Maryam Mohammadi
- Department of Management and Health Information Technology, School of Management and Medical Information Sciences, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Mohammadi
- Department of Electrical Engineering, Shahreza University, Isfahan, Iran
| | - Nahid Ardalan
- Kurdistan University of Medical Sciences, Sanandaj, Kurdistan, Iran
| | | | | | | | | | | | - Marzieh Najafi
- Isfahan Endocrine and Metabolism Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Zeynab Yavari
- Genetic and Environmental Adventures Research Center, School of Abarkouh Paramedicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Farideh Mohammadi
- Department of Textile Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Hakimeh Teiri
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran; Environment Research Center, Research Institute for Primordial Prevention of Non-communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mahsa Jannati
- Graduate Student, Dept. of Civil Engineering, Lakehead University, Thunder Bay, ON, Canada
| |
Collapse
|
5
|
Modeling and sensitivity analysis of the alkylphenols removal via moving bed biofilm reactor using artificial neural networks: Comparison of levenberg marquardt and particle swarm optimization training algorithms. Biochem Eng J 2020. [DOI: 10.1016/j.bej.2020.107685] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
|
6
|
Mohammadi F, Samaei MR, Azhdarpoor A, Teiri H, Badeenezhad A, Rostami S. Modelling and Optimizing Pyrene Removal from the Soil by Phytoremediation using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm. CHEMOSPHERE 2019; 237:124486. [PMID: 31398609 DOI: 10.1016/j.chemosphere.2019.124486] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 07/11/2019] [Accepted: 07/29/2019] [Indexed: 05/26/2023]
Abstract
This study aimed to model and optimize pyrene removal from the soil contaminated by sorghum bicolor plant using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) with Genetic Algorithm (GA) approach. Here, the effects of indole acetic acid (IAA) and pseudomonas aeruginosa bacteria on increasing pyrene removal efficiency by phytoremediation process was studied. The experimental design was done using the Box-Behnken Design (BBD) technique. In the RSM model, the non-linear second-order model was in good agreement with the laboratory results. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one. Existence of eight neurons in the hidden layer leads to the highest R and lowest MSE and MAE. The results of the GA determined the optimum performance conditions. The results showed that using indole acetic acid and pseudomonas bacteria increased the efficiency of the sorghum plant in removing pyrene from the soil. The comparison obviously indicated that the prediction capability of the ANN model was much better than that of the RSM model.
Collapse
Affiliation(s)
- Farzaneh Mohammadi
- Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mohammad Reza Samaei
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| | - Abooalfazl Azhdarpoor
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hakimeh Teiri
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ahmad Badeenezhad
- Department of Environmental Health Engineering, School of Health, Behbahan Faculty of Medical Sciences, Behbahan, Iran
| | - Saeid Rostami
- Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
| |
Collapse
|