作者: Wang, LC (Wang, Lunche); Kisi, O (Kisi, Ozgur); Zounemat-Kermani, M (Zounemat-Kermani, Mohammad); Hu, B (Hu, Bo); Gong, W (Gong, Wei)
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摘要: Long-term hourly observations of photosynthetically active radiation (PAR), global solar radiation (E-g) and meteorological variables [air temperature (T-A), relative humidity (R-H), dew point (T-D), water vapor pressure (V-W), air pressure (P-A)] observed at different types of ecosystems (agricultural farmland, wetland, forest, bay, grassland, desert and lake) in China are reported for developing and validating PAR estimating models. Three improved Artificial Neural Network (ANN) methods, Multilayer Perceptron (MLP), Generalized Regression Neural Network (GRNN), and Radial Basis Neural Network (RBNN) are proposed in this study for predicting the hourly PAR using the combinations of above meteorological variables as model inputs. The ANN models have been compared with an efficient all-sky PAR model (ALSKY) through statistical indicies root mean square errors (RMSE) and mean absolute errors (MAE) at each station. The effects of meteorological variables on the hourly PAR predictions are further analyzed for investigating the main influencing factors for each model. The results indicate that there are large differences in model accuracy for each model at each ecosystem, for example, the MLP and RBNN models whose inputs are the E-g and T-A (RMSE, MAE and R-2 are 7.12, 5.24 and 98.90, respectively) perform better than the GRNN and ALSYK models at the agricultural farmland AKA station, while the GRNN model (RMSE and MAE are 12.47 and 8.98, respectively) performs better than other methods at DHL station. The model inputs also play different roles in different ecosystems for each ANN model, for example, T-A and P-A generally have more effects than the R-H, T-D and V-W variables in the farmland stations, while RH is more important for hourly PAR prediction than the other variables in the bay stations. Finally, the overall rank of the model accuracy is obtained, MLP and RBNN models are more accurate for estimating hourly PAR at various ecosystems in China. (C) 2015 Elsevier Ltd. All rights reserved.
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