干旱区生态环境知识资源中心(Arid): Application of inclusive multiple model for the prediction of saffron water footprint

Arid

DOI	10.1016/j.agwat.2022.108125
	Application of inclusive multiple model for the prediction of saffron water footprint
	Moshizi, Zahra Gerkani Nezhad; Bazrafshan, Ommolbanin; Etedali, Hadi Ramezani; Esmaeilpour, Yahya; Collins, Brain
通讯作者	Bazrafshan, O
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2023
卷号	277
英文摘要	Applying new approaches in the management of water resources is a vital issue, especially in arid and semi-arid regions. The water footprint is a key index in water management. Therefore, it is necessary to predict its changes for future durations. The soft computing model is one of the most widely used models in predicting and estimating agroclimatic variables. The purpose of this study is to predict the green and blue water footprints of saffron product using the soft computing model. In order to select the most effective variables in prediction water footprints, the individual input was eliminated one by one and the effect of each on the residual mean square error (RMSE) was measured. In the first stage, the Group Method of Data Handling (GMDH) and evolutionary algorithms have been applied. In the next stage, the output of individual models was incorporated into the Inclusive Multiple Model (IMM) as the input variables in order to predict the blue and green water footprints of saffron product in three homogenous agroclimatic regions. Finally, the uncertainty of the model caused by the input and parameters was evaluated. The contributions of this research are introducing optimized GMDH and new ensemble models for predicting BWF, and GWF, uncertainty analysis and investigating effective inputs on the GWF and BWF. The results indicated that the most important variables affecting green and blue water footprints are plant transpiration, evapotranspiration, and yield, since removing these variables significantly increased the RMSE (range=11-25). Among the GMDH models, the best performance belonged to NMRA (Naked Mole Ranked Algorithm) due to the fast convergence and high accuracy of the outputs. In this regard, the IMM has a better performance (FSD=0.76, NSE=0.95, MAE) = 8, PBIAS= 8) than the alternatives due to applying the outputs of several individual models and the lowest uncertainty based on the parameters and inputs of the model (p = 0.98, r = 0.08).
英文关键词	Water footprint Saffron Crop and climate variables Group method of data handling Evolutionary algorithms
类型	Article
语种	英语
开放获取类型	Green Accepted, hybrid
收录类别	SCI-E
WOS记录号	WOS:000918168200001
WOS关键词	CONSUMPTION
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395034
推荐引用方式 GB/T 7714	Moshizi, Zahra Gerkani Nezhad,Bazrafshan, Ommolbanin,Etedali, Hadi Ramezani,et al. Application of inclusive multiple model for the prediction of saffron water footprint[J],2023,277.
APA	Moshizi, Zahra Gerkani Nezhad,Bazrafshan, Ommolbanin,Etedali, Hadi Ramezani,Esmaeilpour, Yahya,&Collins, Brain.(2023).Application of inclusive multiple model for the prediction of saffron water footprint.AGRICULTURAL WATER MANAGEMENT,277.
MLA	Moshizi, Zahra Gerkani Nezhad,et al."Application of inclusive multiple model for the prediction of saffron water footprint".AGRICULTURAL WATER MANAGEMENT 277(2023).