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Babak Mohammadi

Doktorand

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Soil moisture estimation using novel bio-inspired soft computing approaches

Författare

  • Roozbeh Moazenzadeh
  • Babak Mohammadi
  • Mir Jafar Sadegh Safari
  • Kwok wing Chau

Summary, in English

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.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap

Publiceringsår

2022

Språk

Engelska

Sidor

826-840

Publikation/Tidskrift/Serie

Engineering Applications of Computational Fluid Mechanics

Volym

16

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

Hong Kong Polytechnic University

Ämne

  • Water Engineering
  • Physical Geography

Nyckelord

  • Hydrological modeling
  • Artificial intelligence
  • data-driven models
  • meteorological variables
  • soil moisture
  • Water resources management

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 1994-2060