Implementing novel hybrid models to improve indirect measurement of the daily soil temperature : Elman neural network coupled with gravitational search algorithm and ant colony optimization
Mehdizadeh, Saeid ; Mohammadi, Babak LU
; Bao Pham, Quoc
; Nguyen Khoi, Dao
and Thi Thuy Linh, Nguyen
(2020)
In Measurement: Journal of the International Measurement Confederation
165.
- Abstract
Soil temperature (ST) as a vital variable of soil plays a key role in agriculture products, surface energy transactions, soil moisture balance, etc. In developing countries like Iran, access to the ST data may be limited. Hence, estimating this parameter by an appropriate alternative approach is of great importance. Two novel hybrid models are developed in this study based on Elman neural network (ENN) coupled with gravitational search algorithm (GSA) and ant colony optimization (ACO) for improving the daily ST estimation at various soil depths (i.e., ENN-GSA and ENN-ACO). In fact, both the optimization algorithms including the GSA and ACO were applied to train the parameters of ENN. To achieve this, the daily data from two stations,... (More)
Soil temperature (ST) as a vital variable of soil plays a key role in agriculture products, surface energy transactions, soil moisture balance, etc. In developing countries like Iran, access to the ST data may be limited. Hence, estimating this parameter by an appropriate alternative approach is of great importance. Two novel hybrid models are developed in this study based on Elman neural network (ENN) coupled with gravitational search algorithm (GSA) and ant colony optimization (ACO) for improving the daily ST estimation at various soil depths (i.e., ENN-GSA and ENN-ACO). In fact, both the optimization algorithms including the GSA and ACO were applied to train the parameters of ENN. To achieve this, the daily data from two stations, namely the Isfahan and Rasht located in Iran were employed during 1998–2017. The classical ENN and hybrid ENN-GSA and ENN-ACO models are developed using the other meteorological parameters under eleven different scenarios. The results illustrated that the proposed hybrid models outperformed the classical ENN for estimating the daily ST of the studied locations at different depths; however, the hybrid ENN-GSA was the best-performing model at the studied stations and whole the soil depths. In addition, all the standalone and hybrid models illustrated the highest accuracy under full-input pattern.
(Less)
- author
- Mehdizadeh, Saeid
; Mohammadi, Babak
LU
; Bao Pham, Quoc
; Nguyen Khoi, Dao
and Thi Thuy Linh, Nguyen
- publishing date
- 2020-12-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Ant colony optimization, Elman neural networks, Estimation, Gravitational search algorithm, Soil temperature
- in
- Measurement: Journal of the International Measurement Confederation
- volume
- 165
- article number
- 108127
- publisher
- Elsevier
- external identifiers
-
- scopus:85086829026
- ISSN
- 0263-2241
- DOI
- 10.1016/j.measurement.2020.108127
- language
- English
- LU publication?
- no
- id
- d6d84161-324c-4dc1-973a-eaeb2ec95597
- date added to LUP
- 2020-12-30 05:16:19
- date last changed
- 2022-04-26 22:52:49
@article{d6d84161-324c-4dc1-973a-eaeb2ec95597,
abstract = {{<p>Soil temperature (ST) as a vital variable of soil plays a key role in agriculture products, surface energy transactions, soil moisture balance, etc. In developing countries like Iran, access to the ST data may be limited. Hence, estimating this parameter by an appropriate alternative approach is of great importance. Two novel hybrid models are developed in this study based on Elman neural network (ENN) coupled with gravitational search algorithm (GSA) and ant colony optimization (ACO) for improving the daily ST estimation at various soil depths (i.e., ENN-GSA and ENN-ACO). In fact, both the optimization algorithms including the GSA and ACO were applied to train the parameters of ENN. To achieve this, the daily data from two stations, namely the Isfahan and Rasht located in Iran were employed during 1998–2017. The classical ENN and hybrid ENN-GSA and ENN-ACO models are developed using the other meteorological parameters under eleven different scenarios. The results illustrated that the proposed hybrid models outperformed the classical ENN for estimating the daily ST of the studied locations at different depths; however, the hybrid ENN-GSA was the best-performing model at the studied stations and whole the soil depths. In addition, all the standalone and hybrid models illustrated the highest accuracy under full-input pattern.</p>}},
author = {{Mehdizadeh, Saeid and Mohammadi, Babak and Bao Pham, Quoc and Nguyen Khoi, Dao and Thi Thuy Linh, Nguyen}},
issn = {{0263-2241}},
keywords = {{Ant colony optimization; Elman neural networks; Estimation; Gravitational search algorithm; Soil temperature}},
language = {{eng}},
month = {{12}},
publisher = {{Elsevier}},
series = {{Measurement: Journal of the International Measurement Confederation}},
title = {{Implementing novel hybrid models to improve indirect measurement of the daily soil temperature : Elman neural network coupled with gravitational search algorithm and ant colony optimization}},
url = {{http://dx.doi.org/10.1016/j.measurement.2020.108127}},
doi = {{10.1016/j.measurement.2020.108127}},
volume = {{165}},
year = {{2020}},
}