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The transferability of random forest and support vector machine for estimating daily global solar radiation using sunshine duration over different climate zones

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Abstract

The transferability of random forest (RF) and support vector machine (SVM) for estimating daily global solar radiation using long-term data of measured sunshine duration, extraterrestrial solar radiation, and theoretical sunshine duration was evaluated across different climate zones. Root mean square error (RMSE), Pearson correlation coefficient (R), and Lin’s concordance correlation coefficient (LCCC) were applied to evaluate model transferability performance. Generally, RF and SVM gave better transfer performance in the climate zone where they were developed. On average, RF (RMSE = 0.881 kWh/m2, R = 0.918, LCCC = 0.885) performed better than SVM (RMSE = 0.93 kWh/m2, R = 0.913, LCCC = 0.87) over the study area. RF had narrow ranges of RMSE, R, and LCCC, indicating that RF was more stable for transfer. The transferability performance of RF was mainly affected by the difference in elevation between source and target sites, and SVM was mostly controlled by the distance and difference in elevation between source and target sites. The results indicated that RF might be applied to estimate daily global solar radiation using sunshine duration at the sites within 500 km distance and 1000 m difference in elevation, and SVM within 500 km distance and 500 m difference in elevation between source and target sites.

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Data availability

Data are available with the corresponding author.

Code availability

Not applicable.

Abbreviations

A:

Equatorial climate zone

ANFIS:

Adaptive neuro-fuzzy inference system

ANN:

Artificial neural network

B:

Arid climate zone

C:

Warm temperature climate zone

Ds:

Distance between source and target sites

D:

Snow climate zone

dElevation:

Difference in elevation between source and target sites

dN:

Difference in sunshine duration between source and target sites

E:

Polar climate zone

LCCC:

Lin’s concordance correlation coefficient

MDA:

Mean decrease in accuracy

MDG:

Mean decrease in Gini

MLP:

Multi-layer perceptron

MLR:

Multiple linear regression

mtry:

Number of variables used at each split

N:

Sunshine duration

No:

Theoretical sunshine duration

ntree:

Number of trees for random forest

R :

Pearson correlation coefficient

RBF:

Radial basis function

RF:

Random forest

RMSE:

Root mean square error

Ro:

Extraterrestrial solar radiation

Rs:

Global solar radiation

SVM:

Support vector machine

Szone:

Zone where models are trained

Tzone:

Zone where models are tested

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Acknowledgements

We would like to thank the National Meteorological Information Center of China Meteorological Administration for providing the meteorological data.

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, 400716, China

    Wei Wu

  2. Institute of Science and Technical Information, Hainan Provincial Key Laboratory of Practical Research On Tropical Crops Information Technology, Chinese Academy of Tropical Agriculture Sciences, Haikou, 571101, China

    Mao-Fen Li

  3. Gansu Meteorological Information and Technical Equipment Support Center, Lanzhou, 730020, China

    Xia Xu

  4. Shapingba Meteorological Bureau, Chongqing, 400030, China

    Xiao-Ping Tang

  5. Chongqing Tobacco Research Institute, Southwest University, Chongqing, 400716, China

    Chao Yang

  6. College of Resources and Environment, Southwest University, Chongqing, 400716, China

    Hong-Bin Liu

Authors
  1. Wei Wu
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  2. Mao-Fen Li
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  3. Xia Xu
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  4. Xiao-Ping Tang
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  5. Chao Yang
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Contributions

Methodology: Wei Wu and Mao-Fen Li; resources: Xiao-Ping Tang, Mao-Fen Li, and Hong-Bin Liu; supervision: Hong-Bin Liu; writing: Wei Wu, Mao-Fen Li, Xia Xu, Xiao-Ping Tang, Chao Yang, and Hong-Bin Liu.

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Correspondence to Hong-Bin Liu.

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Wu, W., Li, MF., Xu, X. et al. The transferability of random forest and support vector machine for estimating daily global solar radiation using sunshine duration over different climate zones. Theor Appl Climatol 146, 45–55 (2021). https://doi.org/10.1007/s00704-021-03726-6

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