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- Title
Prediction of diffuse photosynthetically active radiation using different soft computing techniques.
- Authors
Wang, Lunche; Hu, Bo; Kisi, Ozgur; Zounemat‐Kermani, Mohammad; Gong, Wei
- Abstract
Knowledge of diffuse photosynthetically active radiation ( PAR d) is important for many applications dealing with climate change, environmental engineering and terrestrial productivity. It is necessary to estimate the PAR d using different techniques due to the absence of direct observations of this radiometric flux in most parts of the world. In this study, Adaptive Neuro-Fuzzy Inference Systems ( ANFIS) with grid partition ( ANFIS-GP), ANFIS with subtractive clustering ( ANFIS-SC) and M5 model tree ( M5Tree) are optimized and applied for predicting hourly PAR d in different ecosystems. Hourly climatic variables at six stations from the AmeriFlux network are used for training, validating and testing the above models. It is observed that the mean bias error ( MBE) values range −11 to +18%, −16 to +9% and −10 to +9% for ANFIS-GP, ANFIS-SC and M5Tree models, respectively; root mean square errors ( RMSE) range 25-44%, 22-41% and 29-51%, respectively for different stations in the testing period. The results show that the ANFIS-SC model can generally bring more accurate estimations than other models, and the statistical indices ( MBE and RMSE) at CA_Gro stations (mixed forests) are lower than other stations for each model. The models underestimate some high PAR d values (>900 µmol m−2 s−1) for some stations, which may due to the differences of data ranges and distributions. This study will lay the foundation for accurately mapping regional and global distributions of PAR d and its associated ecological applications.
- Subjects
ENVIRONMENTAL engineering; CLIMATE change; PHOTOSYNTHETICALLY active radiation (PAR); SOFT computing; RADIOMETRY
- Publication
Quarterly Journal of the Royal Meteorological Society, 2017, Vol 143, Issue 706, p2235
- ISSN
0035-9009
- Publication type
Article
- DOI
10.1002/qj.3081