PMID- 32016876
OWN - NLM
STAT- MEDLINE
DCOM- 20200710
LR  - 20210110
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 27
IP  - 12
DP  - 2020 Apr
TI  - Application of hybrid ANN-whale optimization model in evaluation of the field 
      capacity and the permanent wilting point of the soils.
PG  - 13131-13141
LID - 10.1007/s11356-020-07868-4 [doi]
AB  - 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, R(2), and RRMSE for the 
      FC, and 1.78%, 0.92, and 10.02% respectively for RMSE, R(2), 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.
FAU - Vaheddoost, Babak
AU  - Vaheddoost B
AUID- ORCID: 0000-0002-4767-6660
AD  - Department of Civil Engineering, Bursa Technical University, Bursa, Turkey.
FAU - Guan, Yiqing
AU  - Guan Y
AD  - College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, 
      China.
FAU - Mohammadi, Babak
AU  - Mohammadi B
AUID- ORCID: 0000-0001-8427-5965
AD  - College of Hydrology and Water Resources, Hohai University, Nanjing, 210098, 
      China. Babakmohammadi@aol.com.
LA  - eng
PT  - Journal Article
DEP - 20200203
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
RN  - 0 (Soil)
SB  - IM
MH  - Animals
MH  - Azerbaijan
MH  - Iran
MH  - Oman
MH  - *Soil
MH  - *Whales
OTO - NOTNLM
OT  - Hybrid model
OT  - Hydropedology
OT  - Meta-heuristic algorithm
OT  - Soil moisture
EDAT- 2020/02/06 06:00
MHDA- 2020/07/11 06:00
CRDT- 2020/02/05 06:00
PHST- 2019/08/08 00:00 [received]
PHST- 2020/01/24 00:00 [accepted]
PHST- 2020/02/06 06:00 [pubmed]
PHST- 2020/07/11 06:00 [medline]
PHST- 2020/02/05 06:00 [entrez]
AID - 10.1007/s11356-020-07868-4 [pii]
AID - 10.1007/s11356-020-07868-4 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2020 Apr;27(12):13131-13141. doi: 
      10.1007/s11356-020-07868-4. Epub 2020 Feb 3.