Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle

Deo, Ravinesh C.; Downs, Nathan; Parisi, Alfio V.; Adamowski, Jan; Quilty, John

doi:10.1016/j.envres.2017.01.035

Cited by 76 publications

(58 citation statements)

References 105 publications

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“…After testing several values, the minimum split, smoothing, and split threshold were set to 2.0, 15, and 0.05, respectively. These values concur with those assigned in earlier studies (Deo et al., ; Wang, ). Furthermore, the pruning option was applied to implement the “divide‐and‐conquer rule” (Kisi, ; Rahimikhoob et al., ).…”

Section: Study Area Materials and Methodologysupporting

confidence: 93%

“…M5 Tree algorithm (Quinlan, ) is based on a binary decision process (Deo et al., ) (Figure c). It splits input‐target data and applies linear regression at the terminal (leaf) nodes to develop a set of predictive relationships (Mitchell, ).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Data-driven approaches using input datasets (initial conditions and mathematical equations are not required as with empirical and physical models) can be trained and adapted to variable climates for modeling PAR at the local scale. As a result, data-driven models have recently emerged as pertinent tools in energy systems (e.g., (Deo & Sahin, 2017;Deo, Downs, Parisi, Adamowski, & Quilty, 2017;Deo, Wen, & Feng, 2016;Ghimire et al, 2018;Hu, Wang, & Zeng, 2013)). Such models offer added opportunity to forecast future PAR on very short-and long-term scales, without needing extensive atmospheric model initialization conditions.…”

Section: Background Reviewmentioning

confidence: 99%

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Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

Deo

Downs

Adamowski

et al. 2018

Food and Energy Security

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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.

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Section: Study Area Materials and Methodologysupporting

confidence: 93%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Background Reviewmentioning

confidence: 99%

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Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

Deo

Downs

Adamowski

et al. 2018

Food and Energy Security

Self Cite

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show abstract

“…X X Alvear, et al [34] X X Wong, et al [35] X X X X X Peckens, et al [36] X Segura-Garcia, et al [37] X Zhao, et al [38] X X X X Deo, et al [39] X Ekeu-wei, et al [40] X X Bačić, Jogun and Majić…”

Section: Classification Of Urban Health and Medical Datamentioning

confidence: 99%

Redefining the Use of Big Data in Urban Health for Increased Liveability in Smart Cities

2019

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Policy decisions and urban governance are being influenced by an emergence of data from internet of things (IoT), which forms the backbone of Smart Cities, giving rise to Big Data which is processed and analyzed by Artificial Intelligence models at speeds unknown to mankind decades ago. This is providing new ways of understanding how well cities perform, both in terms of economics as well as in health. However, even though cities have been increasingly digitalized, accelerated by the concept of Smart Cities, the exploration of urban health has been limited by the interpretation of sensor data from IoT devices, omitting the inclusion of data from human anatomy and the emergence of biological data in various forms. This paper advances the need for expanding the concept of Big Data beyond infrastructure to include that of urban health through human anatomy; thus, providing a more cohesive set of data, which can lead to a better knowledge as to the relationship of people with the city and how this pertains to the thematic of urban health. Coupling both data forms will be key in supplementing the contemporary notion of Big Data for the pursuit of more contextualized, resilient, and sustainable Smart Cities, rendering more liveable fabrics, as outlined in the Sustainable Development Goal (SDG) 11 and the New Urban Agenda.

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“…机器学习方法还涵盖太阳物理研究的多个方面, 如磁场、暗条、冕洞、日冕亮点等 [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] . 表4 [49][50][51][52][53] 给出了机器学习方法在这些方面的比较有代表性的工作.…”

Section: 其他应用unclassified

The application of machine learning in solar physics

Liu

Jin

2019

Sci. Sin.-Phys. Mech. Astron.

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Very short-term reactive forecasting of the solar ultraviolet index using an extreme learning machine integrated with the solar zenith angle

Cited by 76 publications

References 105 publications

Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

Adaptive Neuro‐Fuzzy Inference System integrated with solar zenith angle for forecasting sub‐tropical Photosynthetically Active Radiation

Redefining the Use of Big Data in Urban Health for Increased Liveability in Smart Cities

The application of machine learning in solar physics

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