FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2912697538> ?p ?o ?g. }

• W2912697538 endingPage "261" @default.
• W2912697538 startingPage "235" @default.
• W2912697538 abstract "Abstract Global advocacy to mitigate climate change impacts on pristine environments, wildlife, ecology, and health has led scientists to design technologies that harness solar energy with remotely sensed, freely available data. This paper presents a study that designed a regionally adaptable and predictively efficient extreme learning machine (ELM) model to forecast long-term incident solar radiation (ISR) over Australia. The relevant satellite-based input data extracted from the Moderate Resolution Imaging Spectroradiometer (i.e., normalized vegetation index, land-surface temperature, cloud top pressure, cloud top temperature, cloud effective emissivity, cloud height, ozone and near infrared-clear water vapour), enriched by geo-temporal input variables (i.e., periodicity, latitude, longitude and elevation) are applied for a total of 41 study sites distributed approximately uniformly and paired with ground-based ISR (target). Of the 41 sites, 26 are incorporated in an ELM algorithm for the design of a universal model, and the remainder are used for model cross-validation. A universally-trained ELM (with training data as a global input matrix) is constructed, and the spatially-deployable model is applied at 15 test sites. The optimal ELM model is attained by trial and error to optimize the hidden layer activation functions for feature extraction and is benchmarked with competitive artificial intelligence algorithms: random forest (RF), M5 Tree, and multivariate adaptive regression spline (MARS). Statistical metrics show that the universally-trained ELM model has very good accuracy and outperforms RF, M5 Tree, and MARS models. With a distinct geographic signature, the ELM model registers a Legates & McCabe's Index of 0.555–0.896 vs. 0.411–0.858 (RF), 0.434–0.811 (M5 Tree), and 0.113–0.868 (MARS). The relative root-mean-square (RMS) error of ELM is low, ranging from approximately 3.715–7.191% vs. 4.907–10.784% (RF), 7.111–11.169% (M5 Tree) and 4.591–18.344% (MARS). Taylor diagrams that illustrate model preciseness in terms of RMS centred difference, error analysis, and boxplots of forecasted vs. observed ISR also confirmed the versatility of the ELM in generating forecasts over heterogeneous, remote spatial sites. This study ascertains that the proposed methodology has practical implications for regional energy modelling, particularly at national scales by utilizing remotely-sensed satellite data, and thus, may be useful for energy feasibility studies at future solar-powered sites. The approach is also important for renewable energy exploration in data-sparse or remote regions with no established measurement infrastructure but with a rich and viable satellite footprint." @default.
• W2912697538 created "2019-02-21" @default.
• W2912697538 creator A5015639176 @default.
• W2912697538 creator A5029315623 @default.
• W2912697538 creator A5065141057 @default.
• W2912697538 creator A5091890459 @default.
• W2912697538 date "2019-04-01" @default.
• W2912697538 modified "2023-10-02" @default.
• W2912697538 title "Universally deployable extreme learning machines integrated with remotely sensed MODIS satellite predictors over Australia to forecast global solar radiation: A new approach" @default.
• W2912697538 cites W1520983743 @default.
• W2912697538 cites W1598241312 @default.
• W2912697538 cites W1696482512 @default.
• W2912697538 cites W1740585449 @default.
• W2912697538 cites W1964727452 @default.
• W2912697538 cites W1964896625 @default.
• W2912697538 cites W1965867673 @default.
• W2912697538 cites W1966490307 @default.
• W2912697538 cites W1969394913 @default.
• W2912697538 cites W1980225742 @default.
• W2912697538 cites W1980318860 @default.
• W2912697538 cites W1984915657 @default.
• W2912697538 cites W1985479415 @default.
• W2912697538 cites W1992658462 @default.
• W2912697538 cites W1995282508 @default.
• W2912697538 cites W2000372390 @default.
• W2912697538 cites W2002404570 @default.
• W2912697538 cites W2004243167 @default.
• W2912697538 cites W2008498216 @default.
• W2912697538 cites W2008684315 @default.
• W2912697538 cites W2016897253 @default.
• W2912697538 cites W2017214666 @default.
• W2912697538 cites W2020236162 @default.
• W2912697538 cites W2020564882 @default.
• W2912697538 cites W2027210298 @default.
• W2912697538 cites W2029871071 @default.
• W2912697538 cites W2031946334 @default.
• W2912697538 cites W2033904036 @default.
• W2912697538 cites W2034831846 @default.
• W2912697538 cites W2035939669 @default.
• W2912697538 cites W2037460094 @default.
• W2912697538 cites W2038912303 @default.
• W2912697538 cites W2039049978 @default.
• W2912697538 cites W2045388493 @default.
• W2912697538 cites W2047884674 @default.
• W2912697538 cites W2057657175 @default.
• W2912697538 cites W2060608702 @default.
• W2912697538 cites W2061008992 @default.
• W2912697538 cites W2061522593 @default.
• W2912697538 cites W2065902166 @default.
• W2912697538 cites W2068481519 @default.
• W2912697538 cites W2071889532 @default.
• W2912697538 cites W2073550151 @default.
• W2912697538 cites W2075573792 @default.
• W2912697538 cites W2075891641 @default.
• W2912697538 cites W2076256832 @default.
• W2912697538 cites W2077910286 @default.
• W2912697538 cites W2078248047 @default.
• W2912697538 cites W2081526790 @default.
• W2912697538 cites W2082192822 @default.
• W2912697538 cites W2086707072 @default.
• W2912697538 cites W2093275097 @default.
• W2912697538 cites W2093623508 @default.
• W2912697538 cites W2099631309 @default.
• W2912697538 cites W2102148524 @default.
• W2912697538 cites W2102201073 @default.
• W2912697538 cites W2110604886 @default.
• W2912697538 cites W2111072639 @default.
• W2912697538 cites W2111286455 @default.
• W2912697538 cites W2111449672 @default.
• W2912697538 cites W2117422131 @default.
• W2912697538 cites W2121971770 @default.
• W2912697538 cites W2125520565 @default.
• W2912697538 cites W2133668312 @default.
• W2912697538 cites W2139086914 @default.
• W2912697538 cites W2141695047 @default.
• W2912697538 cites W2142347393 @default.
• W2912697538 cites W2145031401 @default.
• W2912697538 cites W2149048950 @default.
• W2912697538 cites W2149669644 @default.
• W2912697538 cites W2155431069 @default.
• W2912697538 cites W2159893886 @default.
• W2912697538 cites W2161548576 @default.
• W2912697538 cites W2166693625 @default.
• W2912697538 cites W2169278316 @default.
• W2912697538 cites W2180267094 @default.
• W2912697538 cites W2206042549 @default.
• W2912697538 cites W2232574905 @default.
• W2912697538 cites W2262639697 @default.
• W2912697538 cites W2274744025 @default.
• W2912697538 cites W2473622751 @default.
• W2912697538 cites W2509613771 @default.
• W2912697538 cites W2536008880 @default.
• W2912697538 cites W2536053614 @default.
• W2912697538 cites W2540743583 @default.
• W2912697538 cites W2581811121 @default.
• W2912697538 cites W2592903613 @default.
• W2912697538 cites W2627156000 @default.
• W2912697538 cites W2757997884 @default.

• next