A hybrid extreme learning machine approach for modeling the effectiveness of irrigation methods on greenhouse gas emissions

Hossein Dehghanisanij (), Bahman Yargholi, Somayeh Emami, Hojjat Emami and Haruyuki Fujimaki
Additional contact information
Hossein Dehghanisanij: Agricultural Engineering Research Institute
Bahman Yargholi: Agricultural Engineering Research Institute
Somayeh Emami: University of Tabriz
Hojjat Emami: University of Bonab
Haruyuki Fujimaki: Tottori University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 8, No 37, 18799-18818

Abstract: Abstract Indiscriminate use of water resources in the agricultural sector, decrease in precipitation, and increase in greenhouse gases (GHG) are the most influential factors in creating a crisis in meeting the ecological needs of Lake Urmia. In present study, the amount of GHG emissions due to changing irrigation practices was estimated to reduce water consumption. This investigation was carried out in-field using a hybrid approach called the anti-coronavirus optimization (ACVO) algorithm and extreme learning machine (ELM) to estimate carbon dioxide (CO2) levels. The ACVO-ELM approach was considered on a field dataset, applying irrigation-fertilizer, tillage methods, and crop varieties of selected farms located in the Lake Urmia basin from 2020 to 2021. Five various input combinations were introduced to evaluate CO2 emissions. The capability of the ACVO-ELM approach was computed with the statistical indicators of the Nash–Sutcliffe model efficiency coefficient (NSE), root mean square error (RMSE), and root mean square relative error (RRMSE). Field monitoring outcomes demonstrated that by changing irrigation methods, the amount of CO2 emissions in cultivating wheat, tomato, and sugar beet crops was 1766 KgCO2e ha−1, 2917 KgCO2e ha−1, and 3933 KgCO2e ha−1, respectively. Comparing the treatment to the control, the amount of CO2 is dropping by 25%. Modeling results displayed that the ACVO-ELM approach with the average RMSE = 0.009, NSE = 0.975, and RRMSE = 0.075 has good accuracy in estimating CO2 emissions. Model L4 provides a more optimistic estimate of CO2 emissions by applying parameters of irrigation-fertilizer, and tillage methods as model inputs.

Keywords: ACVO-ELM; CO2; Irrigation; Lake Urmia; Water consumption (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10668-024-04644-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:endesu:v:27:y:2025:i:8:d:10.1007_s10668-024-04644-z

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/10668

DOI: 10.1007/s10668-024-04644-z

Access Statistics for this article

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development is currently edited by Luc Hens

More articles in Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().