Modeling and comparison of hourly photosynthetically active radiation in different ecosystems

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• 作者：Lunche Wang^a ; ^b ; ^{wang@cug.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Ozgur Kisi^c ; Mohammad Zounemat-Kermani^d ; Bo Hu^e ; Wei Gong^f ; ^g
• 关键词：Photosynthetically active radiation ; Ecosystems ; Artificial Neural Networks ; Model accuracy ; Comparison ; Meteorological variables ; China
• 刊名：Renewable and Sustainable Energy Reviews
• 出版年：2016
• 出版时间：April 2016
• 年：2016
• 卷：56
• 期：Complete
• 页码：436-453
• 全文大小：3015 K

文摘

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 R² 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 R_H 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.