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Soil moisture estimation using novel bio-inspired soft computing approaches

Moazenzadeh, Roozbeh ; Mohammadi, Babak LU orcid ; Safari, Mir Jafar Sadegh and Chau, Kwok wing (2022) In Engineering Applications of Computational Fluid Mechanics 16(1). p.826-840
Abstract

Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE =... (More)

Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Hydrological modeling, Artificial intelligence, data-driven models, meteorological variables, soil moisture, Water resources management
in
Engineering Applications of Computational Fluid Mechanics
volume
16
issue
1
pages
15 pages
publisher
Hong Kong Polytechnic University
external identifiers
  • scopus:85126854822
ISSN
1994-2060
DOI
10.1080/19942060.2022.2037467
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
id
7eb1798a-38e6-4487-a09b-de5ce60e9fe0
date added to LUP
2022-04-05 08:41:20
date last changed
2024-01-19 19:34:01
@article{7eb1798a-38e6-4487-a09b-de5ce60e9fe0,
  abstract     = {{<p>Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.</p>}},
  author       = {{Moazenzadeh, Roozbeh and Mohammadi, Babak and Safari, Mir Jafar Sadegh and Chau, Kwok wing}},
  issn         = {{1994-2060}},
  keywords     = {{Hydrological modeling; Artificial intelligence; data-driven models; meteorological variables; soil moisture; Water resources management}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{826--840}},
  publisher    = {{Hong Kong Polytechnic University}},
  series       = {{Engineering Applications of Computational Fluid Mechanics}},
  title        = {{Soil moisture estimation using novel bio-inspired soft computing approaches}},
  url          = {{http://dx.doi.org/10.1080/19942060.2022.2037467}},
  doi          = {{10.1080/19942060.2022.2037467}},
  volume       = {{16}},
  year         = {{2022}},
}