A study on environmental issues of blasting using advanced support vector machine algorithms
- Chen, Lihua, Armaghani, Danial, Fakharian, Pouyan, Bhatawdekar, Ramesh, Samui, P., Khandelwal, Manoj, Khedher, Khaled
- Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
- Full Text:
- Reviewed:
- Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022
- Authors: Chen, Lihua , Armaghani, Danial , Fakharian, Pouyan , Bhatawdekar, Ramesh , Samui, P. , Khandelwal, Manoj , Khedher, Khaled
- Date: 2022
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 19, no. 7 (2022), p. 6221-6240
- Full Text:
- Reviewed:
- Description: Air overpressure is a critical negative effect of blasting in construction or production sites and projects. So far, many attempts have been made to prevent or reduce this negative effect on the nearby construction, equipment, or people. While various experiential equations have been proposed to forecast the air overpressure value for determining the blasting area, these models are typically inaccurate and impractical. Due to the recent efforts to predict the air overpressure by employing artificial intelligence techniques, this study developed five support vector machine-based models optimized by some praised optimization techniques, including the moth flame optimization, particle swarm optimization, grey wolf optimization, cuckoo optimization algorithm, and whale optimization algorithm. These algorithms optimize the most important parameters of the support vector machine, including “C” and “gamma”, and improve the performance of this model for air overpressure prediction. The findings showed that the moth flame optimization algorithm is the best optimizer for support vector machine and is suitable for air overpressure prediction. The support vector machine–moth flame optimization model achieved the best R2 (train: 0.9939; test: 0.9941) and comprehensive score (34). On the other hand, the worst model was the support vector machine–particle swarm optimization, which achieved the lowest comprehensive score (13). In addition, all optimization techniques improved the performance of the single support vector machine model. The findings of this study imply that all optimization techniques successfully enhanced the performance of the support vector machine model; however, the moth flame optimization optimizer was the most effective one. The support vector machine–moth flame optimization technique can be employed to solve other mining-related issues. © 2022, Islamic Azad University (IAU). Correction to: A study on environmental issues of blasting using advanced support vector machine algorithms (International Journal of Environmental Science and Technology, (2022), 19, 7, (6221-6240), 10.1007/s13762-022-03999-y): The original version of this article unfortunately contains two mistakes. The spelling of the third author's name was incorrect. The correct name is Pouyan Fakharian (P. Fakharian). Another error was in the acknowledgment section. The correct Grant No. is KJQN202103415. © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2022
Diatom index of Galela Lake, Halmahera, Indonesia in relation to human activities
- Soeprobowati, Tri, Saraswati, Tyas, Jumari, Jumari, Sari, Kenanga, Gell, Peter
- Authors: Soeprobowati, Tri , Saraswati, Tyas , Jumari, Jumari , Sari, Kenanga , Gell, Peter
- Date: 2023
- Type: Text , Journal article
- Relation: International Journal of Environmental Science and Technology Vol. 20, no. 7 (2023), p. 7707-7722
- Full Text: false
- Reviewed:
- Description: Diatoms, silicious microalgae, have been used successfully as bioindicators of water quality assessment in aquatic ecosystems. Diatoms have a degree of tolerance to the water quality and some diatoms are a good indicator for several water quality variables. Diatom indices have been developed to assess river water quality, mostly in Europe. This study aims to apply diatom indices developed in Europe for the tropical lake of Galela adjacent to residential areas influenced by human activities. Galela Lake is one of the biggest lakes in Halmahera Utara, Indonesia with its main functions being domestic water supply, irrigation, fisheries, and tourism. Human activities have impacted the area around the lake. The 90-cm and 85-cm long sediment cores were collected using a piston corer from Site 1 and 2, respectively. Sediment samples were sliced every 5 cm, separated from sediment by adding HCl and H2O2. The diatom valves were identified under a microscope with 1,000 × magnification. The water quality status of each layer was inferred with diatom indices performed using OMNIDIA software version 6.0. Forty-nine and 63 diatoms species were identified from Site 1 and Site 2, respectively. The number of species and diversity of diatoms was higher in the lower layers than those in the upper layers. The preserved diatom assemblages reflect past physical and chemical water quality. Generic Diatom Index and Specific Pollution Sensitivity Index provided the best evidence for change in Galela Lake—they integrated 70–100% of the diatom taxa from the sediment core samples. © 2022, The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University.
- «
- ‹
- 1
- ›
- »