PMID- 36329360
OWN - NLM
STAT- MEDLINE
DCOM- 20221107
LR  - 20221107
IS  - 1573-2959 (Electronic)
IS  - 0167-6369 (Linking)
VI  - 195
IP  - 1
DP  - 2022 Nov 3
TI  - Modeling monthly reference evapotranspiration process in Turkey: application of 
      machine learning methods.
PG  - 67
LID - 10.1007/s10661-022-10662-z [doi]
AB  - In this study, the predictive power of three different machine learning 
      (ML)-based approaches, namely, multi-gene genetic programming (MGGP), M5 model 
      trees (M5Tree), and K-nearest neighbor algorithm (KNN), for long-term monthly 
      reference evapotranspiration (ET(0)) prediction were investigated. The input data 
      consist of monthly solar radiation (R(s)), maximum air temperature (T(max)), and 
      wind speed (W(s)) derived from 163 meteorological stations in Turkey. Different 
      input combinations were created and analyzed. The model's performance was 
      evaluated using criteria such as Nash-Sutcliffe efficiency, Kling-Gupta 
      efficiency, relative root mean squared error, mean absolute percentage error, and 
      determination coefficient. Moreover, Taylor, radar, and boxplot diagrams were 
      created. It was determined that the MGGP model outperformed both the M5Tree and 
      the KNN models. The equation obtained from the MGGP model, for the best-performed 
      combination of R(s)-T(max)-W(s), was presented. The best weather conditions were 
      obtained as 0.029 to 31.814 MJ/m(2), - 5.8 to 45.7  degrees C, and 0.140 to 5.086 m/s for 
      R(s), T(max), and W(s), respectively. It was also found that the R(s) was the 
      most potent input variable for ET(0) estimation while W(s) was the weakest.
CI  - (c) 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
FAU - Bayram, Savas
AU  - Bayram S
AD  - Department of Civil Engineering, Erciyes University, Kayseri, Turkiye.
FAU - Citakoglu, Hatice
AU  - Citakoglu H
AD  - Department of Civil Engineering, Erciyes University, Kayseri, Turkiye. 
      hcitakoglu@erciyes.edu.tr.
LA  - eng
PT  - Journal Article
DEP - 20221103
PL  - Netherlands
TA  - Environ Monit Assess
JT  - Environmental monitoring and assessment
JID - 8508350
SB  - IM
MH  - Turkey
MH  - *Environmental Monitoring
MH  - *Machine Learning
MH  - Wind
MH  - Meteorology
OTO - NOTNLM
OT  - K-nearest neighbor algorithm
OT  - M5 model trees
OT  - Multi-gene genetic programming
OT  - Reference evapotranspiration
OT  - Turkiye
EDAT- 2022/11/05 06:00
MHDA- 2022/11/08 06:00
CRDT- 2022/11/04 00:42
PHST- 2022/06/20 00:00 [received]
PHST- 2022/10/18 00:00 [accepted]
PHST- 2022/11/04 00:42 [entrez]
PHST- 2022/11/05 06:00 [pubmed]
PHST- 2022/11/08 06:00 [medline]
AID - 10.1007/s10661-022-10662-z [pii]
AID - 10.1007/s10661-022-10662-z [doi]
PST - epublish
SO  - Environ Monit Assess. 2022 Nov 3;195(1):67. doi: 10.1007/s10661-022-10662-z.