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Babak Mohammadi

Doctoral student

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Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils


  • Babak Vaheddoost
  • Yiqing Guan
  • Babak Mohammadi

Summary, in English

Field capacity (FC) and permanent wilting point (PWP) are two important properties of the soil when the soil moisture is concerned. Since the determination of these parameters is expensive and time-consuming, this study aims to develop and evaluate a new hybrid of artificial neural network model coupled with a whale optimization algorithm (ANN-WOA) as a meta-heuristic optimization tool in defining the FC and the PWP at the basin scale. The simulated results were also compared with other core optimization models of ANN and multilinear regression (MLR). For this aim, a set of 217 soil samples were taken from different regions located across the West and East Azerbaijan provinces in Iran, partially covering four important basins of Lake Urmia, Caspian Sea, Persian Gulf-Oman Sea, and Central-Basin of Iran. Taken samples included portion of clay, sand, and silt together with organic matter, which were used as independent variables to define the FC and the PWP. A 80–20 portion of the randomly selected independent and dependent variable sets were used in calibration and validation of the predefined models. The most accurate predictions for the FC and PWP at the selected stations were obtained by the hybrid ANN-WOA models, and evaluation criteria at the validation phases were obtained as 2.87%, 0.92, and 2.11% respectively for RMSE, R2, and RRMSE for the FC, and 1.78%, 0.92, and 10.02% respectively for RMSE, R2, and RRMSE for the PWP. It is concluded that the organic matter is the most important variable in prediction of FC and PWP, while the proposed ANN-WOA model is an efficient approach in defining the FC and the PWP at the basin scale.

Publishing year







Environmental Science and Pollution Research





Document type

Journal article




  • Meteorology and Atmospheric Sciences
  • Environmental Sciences


  • Hybrid model
  • Hydropedology
  • Meta-heuristic algorithm
  • Soil moisture




  • ISSN: 0944-1344