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A clustering model based on an evolutionary algorithm for better energy use in crop production

A clustering model based on an evolutionary algorithm for better energy use in crop production
A clustering model based on an evolutionary algorithm for better energy use in crop production
Energy consumption and its negative environmental impacts are of interesting topics in the recent centuries. Agricultural systems are both energy users and suppliers in the form of bio energy and play a key role in world economics as well as food security. A high amount of energy from different sources is used in this sector while researchers who investigated energy flow in crops production especially in developing countries, have reported a high degree of inefficiency. In order to differentiate between efficient and inefficient farms, a clustering model based on imperialist competitive algorithm (ICA) has been developed and the surveyed watermelon farms have been clustered based on three features, i.e. greenhouse gas (GHG) emission, input energy and farm size. The results showed that of the three developed clusters, the best cluster performed 20 and 46 % better than the two other clusters in energy and 22 and 52 % in CO2 emissions. The average of total energy input and GHG emissions for the best cluster were calculated as 43,423 MJ per ha and 8,120 CO2eq. The results of this study demonstrate the successful application of ICA for better use of energy in cropping systems which can lead to a better environmental and energy performance.
Artificial intelligent, Energy use management, Environmental impacts, Farm management, Imperialist competitive algorithm
1436-3240
1921-1935
Khoshnevisan, Benyamin
94c4bade-4438-4d93-8f53-8adc6b525c90
Bolandnazar, Elham
e2da9eed-af33-4ed4-8170-d651ee03523f
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Shamshirband, Shahaboddin
51afdcdf-3852-4b5f-92a0-0143ab9b2b73
Maghsoudlou, Hamid
d7ba6bac-a126-417b-9825-4c33c2fd5d6f
Altameem, Torki A.
5174de33-296c-4217-86ed-4b66e43bb6ac
Gani, Abdullah
fb6c3735-3015-40df-b9e3-e2cdfae39da3
Khoshnevisan, Benyamin
94c4bade-4438-4d93-8f53-8adc6b525c90
Bolandnazar, Elham
e2da9eed-af33-4ed4-8170-d651ee03523f
Barak, Sasan
f82186de-f5b7-4224-9621-a00e7501f2c3
Shamshirband, Shahaboddin
51afdcdf-3852-4b5f-92a0-0143ab9b2b73
Maghsoudlou, Hamid
d7ba6bac-a126-417b-9825-4c33c2fd5d6f
Altameem, Torki A.
5174de33-296c-4217-86ed-4b66e43bb6ac
Gani, Abdullah
fb6c3735-3015-40df-b9e3-e2cdfae39da3

Khoshnevisan, Benyamin, Bolandnazar, Elham, Barak, Sasan, Shamshirband, Shahaboddin, Maghsoudlou, Hamid, Altameem, Torki A. and Gani, Abdullah (2015) A clustering model based on an evolutionary algorithm for better energy use in crop production. Stochastic Environmental Research and Risk Assessment, 29 (8), 1921-1935. (doi:10.1007/s00477-014-0972-6).

Record type: Article

Abstract

Energy consumption and its negative environmental impacts are of interesting topics in the recent centuries. Agricultural systems are both energy users and suppliers in the form of bio energy and play a key role in world economics as well as food security. A high amount of energy from different sources is used in this sector while researchers who investigated energy flow in crops production especially in developing countries, have reported a high degree of inefficiency. In order to differentiate between efficient and inefficient farms, a clustering model based on imperialist competitive algorithm (ICA) has been developed and the surveyed watermelon farms have been clustered based on three features, i.e. greenhouse gas (GHG) emission, input energy and farm size. The results showed that of the three developed clusters, the best cluster performed 20 and 46 % better than the two other clusters in energy and 22 and 52 % in CO2 emissions. The average of total energy input and GHG emissions for the best cluster were calculated as 43,423 MJ per ha and 8,120 CO2eq. The results of this study demonstrate the successful application of ICA for better use of energy in cropping systems which can lead to a better environmental and energy performance.

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More information

e-pub ahead of print date: 18 October 2014
Published date: 1 December 2015
Keywords: Artificial intelligent, Energy use management, Environmental impacts, Farm management, Imperialist competitive algorithm

Identifiers

Local EPrints ID: 434852
URI: http://eprints.soton.ac.uk/id/eprint/434852
ISSN: 1436-3240
PURE UUID: 9ba341e8-d7cc-4450-9f73-ad1588efac69
ORCID for Sasan Barak: ORCID iD orcid.org/0000-0001-7715-9958

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 10 Jan 2022 03:20

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Contributors

Author: Benyamin Khoshnevisan
Author: Elham Bolandnazar
Author: Sasan Barak ORCID iD
Author: Shahaboddin Shamshirband
Author: Hamid Maghsoudlou
Author: Torki A. Altameem
Author: Abdullah Gani

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