The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution
Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificia...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/5346647 |
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| author | Mohamed Khalid AlOmar Faidhalrahman Khaleel Abdulwahab Abdulrazaaq AlSaadi Mohammed Majeed Hameed Mohammed Abdulhakim AlSaadi Nadhir Al-Ansari |
| author_facet | Mohamed Khalid AlOmar Faidhalrahman Khaleel Abdulwahab Abdulrazaaq AlSaadi Mohammed Majeed Hameed Mohammed Abdulhakim AlSaadi Nadhir Al-Ansari |
| author_sort | Mohamed Khalid AlOmar |
| collection | DOAJ |
| description | Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. |
| format | Article |
| id | doaj-art-a6158c5545f04092a39f9e002445559a |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-a6158c5545f04092a39f9e002445559a2025-02-03T01:00:45ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/5346647The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air PollutionMohamed Khalid AlOmar0Faidhalrahman Khaleel1Abdulwahab Abdulrazaaq AlSaadi2Mohammed Majeed Hameed3Mohammed Abdulhakim AlSaadi4Nadhir Al-Ansari5Department of Civil EngineeringDepartment of Civil EngineeringDepartment of Computer Engineering TechnicsDepartment of Civil EngineeringNatural and Medical Sciences Research CenterCivil Engineering DepartmentAir pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.http://dx.doi.org/10.1155/2022/5346647 |
| spellingShingle | Mohamed Khalid AlOmar Faidhalrahman Khaleel Abdulwahab Abdulrazaaq AlSaadi Mohammed Majeed Hameed Mohammed Abdulhakim AlSaadi Nadhir Al-Ansari The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution Advances in Meteorology |
| title | The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution |
| title_full | The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution |
| title_fullStr | The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution |
| title_full_unstemmed | The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution |
| title_short | The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution |
| title_sort | influence of data length on the performance of artificial intelligence models in predicting air pollution |
| url | http://dx.doi.org/10.1155/2022/5346647 |
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