Improving the prediction of global solar radiation using interpretable boosting algorithms coupled SHAP and LIME analysis: a comparative study Improving the prediction of global solar radiation using interpretable boosting algorithms coupled SHAP and LIME analysis: a comparative study

Solar radiation prediction plays a vital role in many areas of hydrological and water resources planning and management. However, the need for a machine learning (ML) model’s interpretability and explainability has motivated the use of various interpretability methods. For these reasons, the present...

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Vydáno v:	Theoretical and applied climatology Ročník 156; číslo 5; s. 292
Hlavní autoři:	Merabet, Khaled, Daif, Noureddine, Di Nunno, Fabio, Granata, Francesco, Difi, Salah, Kisi, Ozgur, Heddam, Salim, Kim, Sungwon, Zounemat-Kermani, Mohammad
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Vienna Springer Vienna 01.05.2025 Springer Nature B.V
Témata:	Algorithms Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climatology Comparative analysis Comparative studies comparative study Earth and Environmental Science Earth Sciences hydrology Machine learning Periodicity prediction Radiation Root-mean-square errors Solar radiation Waste Water Technology Water Management Water Pollution Control Water resources Water resources planning Weather
ISSN:	0177-798X, 1434-4483
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Popis
Shrnutí:	Solar radiation prediction plays a vital role in many areas of hydrological and water resources planning and management. However, the need for a machine learning (ML) model’s interpretability and explainability has motivated the use of various interpretability methods. For these reasons, the present study was oriented toward the development of robust ML models based on boosting algorithms and enhanced using SHapley Addictive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) algorithms. Six boosting algorithms were used in the present study: adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), categorical boosting (CatBoost), light gradient boosting machine (LightGBM), natural gradient boosting (NGBoost), and histogram gradient boosting (HistGBRT). All models were developed using data collected at the USGS 02187010 station and composed from various weather variables. All models were evaluated using root mean squared error (RMSE), the mean absolute error (MAE), the coefficient of correlation (R), and the Nash–Sutcliffe efficiency (NSE), based on two different scenarios: ( i ) scenario 1 using only weather variables, and ( ii ) scenario 2 weather variables combined with periodicity numbers, i.e., day, month, and year number. The obtained results indicate that the proposed boosting models using periodicity outperform the single models without periodicity, and excellent numerical performances were obtained. For scenario 1, the best accuracy was obtained using the CatBoost1 with R, NSE, RMSE, and MAE values of 0.835, 0.697, 44.407 W/m 2 , and 34.721 W/m 2 , respectively. Using scenario 2, the performances of the models were improved, showing the R, NSE, RMSE, and MAE values significantly improved reaching the values of 0.925, 0.856, 30.617 W/m 2 , and 22.925 W/m 2 , respectively, obtained using the CatBoost1 and HistGBRT1. Graphical Abstract
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0177-798X 1434-4483
DOI:	10.1007/s00704-025-05507-x