Application of inclusive multiple model for the prediction of saffron water footprint

Article


Moshizi, Zahra Gerkani Nezhad, Bazrafshan, Ommolbanin, Ramezani Etedali, Hadi, Esmaeilpour, Yahya and Collins, Brian. 2023. "Application of inclusive multiple model for the prediction of saffron water footprint." Agricultural Water Management. 277. https://doi.org/10.1016/j.agwat.2022.108125
Article Title

Application of inclusive multiple model for the prediction of saffron water footprint

ERA Journal ID5245
Article CategoryArticle
AuthorsMoshizi, Zahra Gerkani Nezhad, Bazrafshan, Ommolbanin, Ramezani Etedali, Hadi, Esmaeilpour, Yahya and Collins, Brian
Journal TitleAgricultural Water Management
Journal Citation277
Article Number108125
Number of Pages19
Year2023
PublisherElsevier
Place of PublicationNetherlands
ISSN0378-3774
1873-2283
Digital Object Identifier (DOI)https://doi.org/10.1016/j.agwat.2022.108125
Web Address (URL)https://www.sciencedirect.com/science/article/pii/S0378377422006722?via%3Dihub
Abstract

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).

KeywordsWater footprint ; Saffron; Crop and climate variables ; Group method of data handling ; Evolutionary algorithms
Article Publishing Charge (APC) FundingOther
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400513. Water resources engineering
300210. Sustainable agricultural development
Byline AffiliationsUniversity of Hormozgan, Iran
Imam Khomeini International University, Iran
James Cook University
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