Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine
Soil erosion control is a complex, integrated management process, constructed based on unified planning by adjusting the land use structure, reasonably configuring engineering, plant, and farming measures to form a complete erosion control system, while meeting the laws of soil erosion, economic and...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/17/2115 |
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| author | Chengcheng Chen Xianchang Wang Chengwen Wu Majdi Mafarja Hamza Turabieh Huiling Chen |
| author_facet | Chengcheng Chen Xianchang Wang Chengwen Wu Majdi Mafarja Hamza Turabieh Huiling Chen |
| author_sort | Chengcheng Chen |
| collection | DOAJ |
| description | Soil erosion control is a complex, integrated management process, constructed based on unified planning by adjusting the land use structure, reasonably configuring engineering, plant, and farming measures to form a complete erosion control system, while meeting the laws of soil erosion, economic and social development, and ecological and environmental security. The accurate prediction and quantitative forecasting of soil erosion is a critical reference indicator for comprehensive erosion control. This paper applies a new swarm intelligence optimization algorithm to the soil erosion classification and prediction problem, based on an enhanced moth-flame optimizer with sine–cosine mechanisms (SMFO). It is used to improve the exploration and detection capability by using the positive cosine strategy, meanwhile, to optimize the penalty parameter and the kernel parameter of the kernel extreme learning machine (KELM) for the rainfall-induced soil erosion classification prediction problem, to obtain more-accurate soil erosion classifications and the prediction results. In this paper, a dataset of the Vietnam Son La province was used for the model evaluation and testing, and the experimental results show that this SMFO-KELM method can accurately predict the results, with significant advantages in terms of classification accuracy (ACC), Mathews correlation coefficient (MCC), sensitivity (sensitivity), and specificity (specificity). Compared with other optimizer models, the adopted method is more suitable for the accurate classification of soil erosion, and can provide new solutions for natural soil supply capacity analysis, integrated erosion management, and environmental sustainability judgment. |
| first_indexed | 2024-03-10T08:13:25Z |
| format | Article |
| id | doaj.art-2a3d2e6519ae46b9807e90d936f5b110 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T08:13:25Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-2a3d2e6519ae46b9807e90d936f5b1102023-11-22T10:30:10ZengMDPI AGElectronics2079-92922021-08-011017211510.3390/electronics10172115Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning MachineChengcheng Chen0Xianchang Wang1Chengwen Wu2Majdi Mafarja3Hamza Turabieh4Huiling Chen5College of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Technology, Jilin University, Changchun 130012, ChinaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaDepartment of Computer Science, Birzeit University, P.O. Box 14, West Bank, Birzeit 627, PalestineDepartment of Information Technology, College of Computers and Information Technology, P.O. Box 11099, Taif University, Taif 21944, Saudi ArabiaCollege of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, ChinaSoil erosion control is a complex, integrated management process, constructed based on unified planning by adjusting the land use structure, reasonably configuring engineering, plant, and farming measures to form a complete erosion control system, while meeting the laws of soil erosion, economic and social development, and ecological and environmental security. The accurate prediction and quantitative forecasting of soil erosion is a critical reference indicator for comprehensive erosion control. This paper applies a new swarm intelligence optimization algorithm to the soil erosion classification and prediction problem, based on an enhanced moth-flame optimizer with sine–cosine mechanisms (SMFO). It is used to improve the exploration and detection capability by using the positive cosine strategy, meanwhile, to optimize the penalty parameter and the kernel parameter of the kernel extreme learning machine (KELM) for the rainfall-induced soil erosion classification prediction problem, to obtain more-accurate soil erosion classifications and the prediction results. In this paper, a dataset of the Vietnam Son La province was used for the model evaluation and testing, and the experimental results show that this SMFO-KELM method can accurately predict the results, with significant advantages in terms of classification accuracy (ACC), Mathews correlation coefficient (MCC), sensitivity (sensitivity), and specificity (specificity). Compared with other optimizer models, the adopted method is more suitable for the accurate classification of soil erosion, and can provide new solutions for natural soil supply capacity analysis, integrated erosion management, and environmental sustainability judgment.https://www.mdpi.com/2079-9292/10/17/2115sine–cosine algorithmmoth-flame algorithmkernel extreme learning machineparameter optimizationsoil erosion prediction |
| spellingShingle | Chengcheng Chen Xianchang Wang Chengwen Wu Majdi Mafarja Hamza Turabieh Huiling Chen Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine Electronics sine–cosine algorithm moth-flame algorithm kernel extreme learning machine parameter optimization soil erosion prediction |
| title | Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine |
| title_full | Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine |
| title_fullStr | Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine |
| title_full_unstemmed | Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine |
| title_short | Soil Erosion Prediction Based on Moth-Flame Optimizer-Evolved Kernel Extreme Learning Machine |
| title_sort | soil erosion prediction based on moth flame optimizer evolved kernel extreme learning machine |
| topic | sine–cosine algorithm moth-flame algorithm kernel extreme learning machine parameter optimization soil erosion prediction |
| url | https://www.mdpi.com/2079-9292/10/17/2115 |
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