Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea
Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligen...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Ain Shams Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447922001873 |
| _version_ | 1797897289776234496 |
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| author | Samad Emamgholizadeh Ahmad Bazoobandi Babak Mohammadi Hadi Ghorbani Mohammad Amel Sadeghi |
| author_facet | Samad Emamgholizadeh Ahmad Bazoobandi Babak Mohammadi Hadi Ghorbani Mohammad Amel Sadeghi |
| author_sort | Samad Emamgholizadeh |
| collection | DOAJ |
| description | Cation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model. |
| first_indexed | 2024-04-10T07:55:08Z |
| format | Article |
| id | doaj.art-45e4dbfcec9d415590f91e82c8f1023d |
| institution | Directory Open Access Journal |
| issn | 2090-4479 |
| language | English |
| last_indexed | 2024-04-10T07:55:08Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj.art-45e4dbfcec9d415590f91e82c8f1023d2023-02-23T04:31:09ZengElsevierAin Shams Engineering Journal2090-44792023-03-01142101876Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian SeaSamad Emamgholizadeh0Ahmad Bazoobandi1Babak Mohammadi2Hadi Ghorbani3Mohammad Amel Sadeghi4Department of Water and Environmental, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, IranDepartment of Soil Science, Ferdowsi University of Mashhad, Mashhad, IranDepartment of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, SE-223 62 Lund, Sweden; Corresponding author.Department of Water and Soil Engineering, Faculty of Agriculture, Shahrood University of Technology, Shahrood, IranDepartment of Water Engineering, Faculty of Agriculture, Takestan Branch, Islamic Azad University, Takestan, IranCation exchange capacity (CEC) has a key role in soil studies such as agriculture, energy balance, characteristics of the soil for food, maintaining water in the soil as well as soil pollution management. Its measurement is difficult and time-consuming. So, its prediction using artificial intelligent (AI) models with soil readily available properties can be the proper solution. In this study, the physical and chemical properties of the soil, such as pH, EC, organic carbon, clay content, sands, and total nitrogen used as input data for the AI models. The adaptive-network-based fuzzy inference system (ANFIS), ANFIS model coupled by differential evolution (ANFIS-DE), and ANFIS model coupled by particle swarm optimization (ANFIS-PSO) are used for the prediction of the CEC. Then the ability of those methods in the prediction of the CEC. Results showed higher efficiency of the coupled models (ANFIS-DE and ANFIS-PSO) compared to the ordinary ANFIS model.http://www.sciencedirect.com/science/article/pii/S2090447922001873Artificial intelligence modelCation exchange capacityDifferential evolution algorithmMultidisciplinary researchMultiple soil classesParticle swarm optimization |
| spellingShingle | Samad Emamgholizadeh Ahmad Bazoobandi Babak Mohammadi Hadi Ghorbani Mohammad Amel Sadeghi Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea Ain Shams Engineering Journal Artificial intelligence model Cation exchange capacity Differential evolution algorithm Multidisciplinary research Multiple soil classes Particle swarm optimization |
| title | Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea |
| title_full | Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea |
| title_fullStr | Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea |
| title_full_unstemmed | Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea |
| title_short | Prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the Caspian Sea |
| title_sort | prediction of soil cation exchange capacity using enhanced machine learning approaches in the southern region of the caspian sea |
| topic | Artificial intelligence model Cation exchange capacity Differential evolution algorithm Multidisciplinary research Multiple soil classes Particle swarm optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2090447922001873 |
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