Arid
DOI10.1155/2022/3123475
Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction
Shahabi, Mahmood; Ghorbani, Mohammad Ali; Naganna, Sujay Raghavendra; Kim, Sungwon; Hadi, Sinan Jasim; Inyurt, Samed; Farooque, Aitazaz Ahsan; Yaseen, Zaher Mundher
通讯作者Yaseen, ZM
来源期刊COMPLEXITY
ISSN1076-2787
EISSN1099-0526
出版年2022
卷号2022
英文摘要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 (R-2) = 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.
类型Article
语种英语
开放获取类型gold
收录类别SCI-E
WOS记录号WOS:000817608800002
WOS关键词EXTREME LEARNING-MACHINE ; PEDOTRANSFER FUNCTIONS ; ORGANIC-MATTER ; REGRESSION ; SYSTEM ; CEC
WOS类目Mathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS研究方向Mathematics ; Science & Technology - Other Topics
资源类型期刊论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/392176
推荐引用方式
GB/T 7714
Shahabi, Mahmood,Ghorbani, Mohammad Ali,Naganna, Sujay Raghavendra,et al. Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction[J],2022,2022.
APA Shahabi, Mahmood.,Ghorbani, Mohammad Ali.,Naganna, Sujay Raghavendra.,Kim, Sungwon.,Hadi, Sinan Jasim.,...&Yaseen, Zaher Mundher.(2022).Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction.COMPLEXITY,2022.
MLA Shahabi, Mahmood,et al."Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction".COMPLEXITY 2022(2022).
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