Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction
The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/3123475 |
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| author | Mahmood Shahabi Mohammad Ali Ghorbani Sujay Raghavendra Naganna Sungwon Kim Sinan Jasim Hadi Samed Inyurt Aitazaz Ahsan Farooque Zaher Mundher Yaseen |
| author_facet | Mahmood Shahabi Mohammad Ali Ghorbani Sujay Raghavendra Naganna Sungwon Kim Sinan Jasim Hadi Samed Inyurt Aitazaz Ahsan Farooque Zaher Mundher Yaseen |
| author_sort | Mahmood Shahabi |
| collection | DOAJ |
| description | The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network (MM-GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporation into the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate calcium equivalent (CCE), and soil organic matter (OM) were used as model inputs to predict soil CEC. With the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, silt, and CCE) as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency (NSE) = 0.87, Root Mean Square Error (RMSE) = 2.885, Mean Absolute Error (MAE) = 2.249, Mean Absolute Percentage Error (MAPE) = 12.072, and coefficient of determination (R2) = 0.884. The proposed MM-GANN model is a reliable intelligence-based approach for the assessment of soil quality parameters intended for sustainability and management prospects. |
| format | Article |
| id | doaj-art-c1a20f8b7a3f436eac84151c9849ade7 |
| institution | OA Journals |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-c1a20f8b7a3f436eac84151c9849ade72025-08-20T02:19:14ZengWileyComplexity1099-05262022-01-01202210.1155/2022/3123475Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity PredictionMahmood Shahabi0Mohammad Ali Ghorbani1Sujay Raghavendra Naganna2Sungwon Kim3Sinan Jasim Hadi4Samed Inyurt5Aitazaz Ahsan Farooque6Zaher Mundher Yaseen7Department of Water EngineeringDepartment of Water EngineeringDepartment of Civil EngineeringDepartment of Railroad Construction and Safety EngineeringDepartment of Real Estate Development and ManagementFaculty of Engineering and ArchitectureFaculty of Sustainable Design EngineeringAdjunct Research FellowThe potential of the soil to hold plant nutrients is governed by the cation-exchange capacity (CEC) of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network (MM-GANN) was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models (i.e., artificial neural network (ANN) and extreme learning machine (ELM)) were implemented for incorporation into the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate calcium equivalent (CCE), and soil organic matter (OM) were used as model inputs to predict soil CEC. With the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters (e.g., Clay, OM, pH, silt, and CCE) as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency (NSE) = 0.87, Root Mean Square Error (RMSE) = 2.885, Mean Absolute Error (MAE) = 2.249, Mean Absolute Percentage Error (MAPE) = 12.072, and coefficient of determination (R2) = 0.884. The proposed MM-GANN model is a reliable intelligence-based approach for the assessment of soil quality parameters intended for sustainability and management prospects.http://dx.doi.org/10.1155/2022/3123475 |
| spellingShingle | Mahmood Shahabi Mohammad Ali Ghorbani Sujay Raghavendra Naganna Sungwon Kim Sinan Jasim Hadi Samed Inyurt Aitazaz Ahsan Farooque Zaher Mundher Yaseen Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction Complexity |
| title | Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction |
| title_full | Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction |
| title_fullStr | Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction |
| title_full_unstemmed | Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction |
| title_short | Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction |
| title_sort | integration of multiple models with hybrid artificial neural network genetic algorithm for soil cation exchange capacity prediction |
| url | http://dx.doi.org/10.1155/2022/3123475 |
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