Spatial Assessment of Solar Radiation by Machine Learning and Deep Neural Network Models Using Data Provided by the COMS MI Geostationary Satellite: A Case Study in South Korea
Autor: | Jin-Young Kim, Tae-Byeong Chae, Jong-Min Yeom, Chang-Suk Lee, Seonyoung Park |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Pyranometer
010504 meteorology & atmospheric sciences Computer science COMS MI 020209 energy solar radiation 02 engineering and technology Radiation Machine learning computer.software_genre lcsh:Chemical technology 01 natural sciences Biochemistry Article Analytical Chemistry 0202 electrical engineering electronic engineering information engineering support vector machine lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation 0105 earth and related environmental sciences Artificial neural network business.industry deep neural network Atomic and Molecular Physics and Optics Geostationary orbit Common spatial pattern Satellite Artificial intelligence business computer artificial neural network random forest |
Zdroj: | Sensors, Vol 19, Iss 9, p 2082 (2019) Sensors Volume 19 Issue 9 Sensors (Basel, Switzerland) |
ISSN: | 1424-8220 |
Popis: | Although data-driven methods including deep neural network (DNN) were introduced, there was not enough assessment about spatial characteristics when using limited ground observation as reference. This work aimed to interpret the feasibility of several machine learning approaches to assess the spatial distribution of solar radiation on Earth based on the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) geostationary satellite. Four data-driven models were selected (artificial neural network (ANN), random forest (RF), support vector regression (SVR), and DNN), to compare their accuracy and spatial estimating performance. Moreover, we used a physical model to probe the ability of data-driven methods, implementing hold-out and k-fold cross-validation approaches based on pyranometers located in South Korea. The results of analysis showed the RF had the highest accuracy in predicting performance, although the difference between RF and the second-best technique (DNN) was insignificant. Temporal variations in root mean square error (RMSE) were dependent on the number of data samples, while the physical model showed relatively less sensitivity. Nevertheless, DNN and RF showed less variability in RMSE than the others. To examine spatial estimation performance, we mapped solar radiation over South Korea for each model. The data-driven models accurately simulated the observed cloud pattern spatially, whereas the physical model failed to do because of cloud mask errors. These exhibited different spatial retrieval performances according to their own training approaches. Overall analysis showed that deeper layers of networks approaches (RF and DNN), could best simulate the challenging spatial pattern of thin clouds when using satellite multispectral data. |
Databáze: | OpenAIRE |
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