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• W2897468516 endingPage "e00151" @default.
• W2897468516 startingPage "e00151" @default.
• W2897468516 abstract "Advocacy for climate mitigation aims to minimize the use of fossil fuel and to support clean energy adaptation. While alternative energies (e.g., biofuels) extracted from feedstock (e.g., micro-algae) represent a promising role, their production requires reliably modeled photosynthetically active radiation (PAR). PAR models predict energy parameters (e.g., algal carbon fixation) to aid in decision-making at PAR sites. Here, we model very short-term (5-min scale), sub-tropical region's PAR with an Adaptive Neuro-Fuzzy Inference System model with a Centroid-Mean (ANFIS-CM) trained with a non-climate input (i.e., only the solar angle, θZ). Accuracy is benchmarked against genetic programming (GP), M5Tree, Random Forest (RF), and multiple linear regression (MLR). ANFIS-CM integrates fuzzy and neural network algorithms, whereas GP adopts an evolutionary approach, M5Tree employs binary decision, RF employs a bootstrapped ensemble, and MLR uses statistical tools to link PAR with θZ. To design the ANFIS-CM model, 5-min θZ (01–31 December 2012; 0500H–1900H) for sub-tropical, Toowoomba are utilized to extract predictive features, and the testing accuracy (i.e., differences between measurements and forecasts) is evaluated with correlation (r), root-mean-square error (RMSE), mean absolute error (MAE), Willmott (WI), Nash–Sutcliffe (ENS), and Legates & McCabes (ELM) Index. ANFIS-CM and GP are equivalent for 5-min forecasts, yielding the lowest RMSE (233.45 and 233.01μ mol m−2s−1) and MAE (186.59 and 186.23 μmol m−2s−1). In contrast, MLR, M5Tree, and RF yields higher RMSE and MAE [(RMSE = 322.25 μmol m−2s−1, MAE = 275.32 μmol m−2s−1), (RMSE = 287.70 μmol m−2s−1, MAE = 234.78 μmol m−2s−1), and (RMSE = 359.91 μmol m−2s−1, MAE = 324.52 μmol m−2s−1)]. Based on normalized error, ANFIS-CM is considerably superior (MAE = 17.18% versus 19.78%, 34.37%, 26.39%, and 30.60% for GP, MLR, M5Tree, and RF models, respectively). For hourly forecasts, ANFIS-CM outperforms all other methods (WI = 0.964 vs. 0.942, 0.955, 0.933 & 0.893, and ELM = 0.741 versus 0.701, 0.728, 0.619 & 0.490 for GP, MLR, M5Tree, and RF, respectively). Descriptive errors support the versatile predictive skills of the ANFIS-CM model and its role in real-time prediction of the photosynthetic-active energy to explore biofuel generation from micro-algae, studying food chains, and supporting agricultural precision." @default.
• W2897468516 created "2018-10-26" @default.
• W2897468516 creator A5008724716 @default.
• W2897468516 creator A5015639176 @default.
• W2897468516 creator A5043334558 @default.
• W2897468516 creator A5065141057 @default.
• W2897468516 date "2018-10-09" @default.
• W2897468516 modified "2023-10-16" @default.
• W2897468516 title "Adaptive Neuro-Fuzzy Inference System integrated with solar zenith angle for forecasting sub-tropical Photosynthetically Active Radiation" @default.
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