Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

Nematchoua, Modeste Kameni; Orosa, José A.; Afaifia, Marwa

doi:10.1016/j.ecoinf.2022.101643

Cited by 28 publications

(10 citation statements)

References 61 publications

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“… Ebrahimy and Azadbakht (2019) revealed that the RFR model shows good model stability, high accuracy, and efficient computation in LST spatial downscaling processes ( Ebrahimy and Azadbakht, 2019 ). For non-spatial studies, Nematchoua et al (2022) revealed that several machine learning algorithms, including RFR, produced predictive models of daily global solar radiation and air temperature in European cities in 2050 and 2100 with good accuracy. ( Nematchoua et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…For non-spatial studies, Nematchoua et al (2022) revealed that several machine learning algorithms, including RFR, produced predictive models of daily global solar radiation and air temperature in European cities in 2050 and 2100 with good accuracy. ( Nematchoua et al, 2022 ). Experiments on several input variables offered in this study can encourage a promising LST spatial downscaling method for urban climate and epidemiological studies.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

Purwanto

Astuti²,

Rohman³

et al. 2022

Ecological Informatics

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

Purwanto

Astuti²,

Rohman³

et al. 2022

Ecological Informatics

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“…Parameter estimation values and their uncertainty converge as the number of measurements increases [148,152], and conventional monitoring lacks comprehensive data, such as groundwater monitoring data [170]. Combining satellite data [140,172] and field observations, with machine learning [49,173] can significantly enhance the capabilities of models.…”

Section: Thermal Stratification Modelsmentioning

confidence: 99%

Challenge to Lake Ecosystems: Changes in Thermal Structure Triggered by Climate Change

Zhang,

Shen,

et al. 2024

Water

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Human activities, global warming, frequent extreme weather events, and changes in atmospheric composition affect the solar radiation reaching the Earth’s surface, affect mass and heat transfer at the air–water interface, and induce oscillations in wind-driven internal waves. This leads to changes in the spatiotemporal characteristics of thermal stratification in lakes, altering lake circulation patterns and vertical mass transfer. However, thermal stratification structures are often overlooked. The intensification of lake thermal stratification due to warming may lead to increased release of bottom pollutants, spreading through the dynamic behavior of the thermocline to the epilimnion. Moreover, the increased heat storage is beneficial for the growth and development of certain phytoplankton, resulting in rapid transitions of the original steady state of lakes. Consequently, water quality deterioration, ecological degradation, and declining biodiversity may occur. Conventional surface water monitoring may not provide comprehensive, accurate, and timely assessments. Model simulations can better predict future thermal stratification behaviors, reducing financial burdens, providing more refined assessments, and thus preventing subsequent environmental issues.

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“…The solar radiation is a meteorological variable which is either not measured or is of low accuracy in many cases. Many empirical methods have been developed and evaluated to predict the solar radiation using daily meteorological parameters [16,17,18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

Antonopoulos,

Antonopoulos

2024

JAERI

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Aims: The reference evapotranspiration (ETo) estimation with Penman-Monteith or Priestley-Taylor methods requires measurements of temperature, radiation, humidity, and wind velocity. In this study, we evaluated the estimations of ETo by Penman-Monteith (ETo-PM) and Priestley-Taylor (ETo-PrT) methods using indirect methods of calculating solar radiation (Rs). Place and Duration of Study: Daily meteorological measurements from two stations in northern Greece were used for the development of solar radiation models and ETo calculation. Methodology: The indirect methods of calculating solar radiation (Rs) are based on Artificial Neural networks (ANN) technology and models using the multi-linear regression method (MLR). Three different ANN and MLR models were derived. The Hargreaves method is also used. The evaluation of the indirect Rs derived models and the ETo estimation by the two methods was performed with the use of correlation coefficients (r), root mean square error (RMSE), and efficiency (EF) indexes. Results: The statistics of ETo estimation at the two stations showed that the r and EF values, between the estimated ETo using the indirect Rs models and estimated ETo using Rs measured, were greater than 0.963 and 0.918, respectively, while the RMSE values were lower than 0.646 mm d-1. The statistics of Rs models, showed that the r and EF values were greater than 0.860 and 0.605, respectively, while the RMSE values were lower than 4.47 MJ m-2d-1. Conclusion: The results of ANN models in comparison to MLR models, when using the same input variables, are consistent between them. These findings indicate that the Penman-Monteith and Priestley-Taylor methods can accurately predict ETo using Rs values estimated indirectly through the examined methods and models.

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Prediction of daily global solar radiation and air temperature using six machine learning algorithms; a case of 27 European countries

Cited by 28 publications

References 61 publications

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

Assessment of the dynamics of urban surface temperatures and air pollution related to COVID-19 in a densely populated City environment in East Java

Challenge to Lake Ecosystems: Changes in Thermal Structure Triggered by Climate Change

Reference Evapotranspiration Evaluation Using Solar Radiation Estimated by ANN and Empirical Models

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