干旱区生态环境知识资源中心(Arid): Integration of Multiple Models with Hybrid Artificial Neural Network-Genetic Algorithm for Soil Cation-Exchange Capacity Prediction

Arid

DOI	10.1155/2022/3123475
	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
ISSN	1076-2787
EISSN	1099-0526
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).