| 1. |
- El-kenawy, El-Sayed M., et al.
(author)
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Sunshine duration measurements and predictions in Saharan Algeria region : an improved ensemble learning approach
- 2022
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In: Journal of Theoretical and Applied Climatology. - : Springer. - 0177-798X .- 1434-4483. ; 147:3-4, s. 1015-1031
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Journal article (peer-reviewed)abstract
- Sunshine duration is an important atmospheric indicator used in many agricultural, architectural, and solar energy applications (photovoltaics, thermal systems, and passive building design). Hence, it should be estimated accurately for areas with low-quality data or unavailable precise measurements. This paper aimed to obtain a sunshine duration measurement database in Algeria’s south region and also to study the applicability of computational models to predict them. This work develops ensemble learning models for assessing daily sunshine duration with meteorological datasets that include daily mean relative humidity, daily mean air temperature, daily maximum air temperature, daily minimum air temperature, and daily temperature range as input. The study proposes a unique hybrid model, combining grey wolf and stochastic fractal search (GWO-SFS) optimization algorithms with the random forest regressor ensemble. A pre-feature selection process improved the newly suggested model. Various commonly adopted algorithms in relevant studies have been considered as references for evaluating the new hybrid algorithm. The accuracy of models was examined as a function of some frequently used statistical pointers, as well as the Wilcoxon rank-sum test. Besides, the models were evaluated according to the several input combinations. The numerical experiments show that the proposed optimization ensemble with feature preprocessing outperforms stand-alone models in terms of prediction accuracy and robustness, where relative root mean square errors are reduced by over 20% for all considered locations. In addition, all correlation coefficients are higher than 0.999. Moreover, the proposed model, with RMSEs lower than 0.4884 hours, shows significantly superior performances compared to previously proposed models in the literature.
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| 2. |
- Jamei, Mehdi, et al.
(author)
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Data-Driven Models for Predicting Solar Radiation in Semi-Arid Regions
- 2023
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In: Computers, Materials and Continua. - : Tech Science Press. - 1546-2218 .- 1546-2226. ; 74:1, s. 1625-1640
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Journal article (peer-reviewed)abstract
- Solar energy represents one of the most important renewable energy sources contributing to the energy transition process. Considering that the observation of daily global solar radiation (GSR) is not affordable in some parts of the globe, there is an imperative need to develop alternative ways to predict it. Therefore, the main objective of this study is to evaluate the performance of different hybrid data-driven techniques in predicting daily GSR in semi-arid regions, such as the majority of Spanish territory. Here, four ensemble-based hybrid models were developed by hybridizing Additive Regression (AR) with Random Forest (RF), Locally Weighted Linear Regression (LWLR), Random Subspace (RS), and M5P. The base algorithms of the developed models are scarcely applied in previous studies to predict solar radiation. The testing phase outcomes demonstrated that the AR-RF models outperform all other hybrid models. The provided models were validated by statistical metrics, such as the correlation coefficient (R) and root mean square error (RMSE). The results proved that Scenario #6, utilizing extraterrestrial solar radiation, relative humidity, wind speed, and mean, maximum, and minimum ambient air temperatures as the model inputs, leads to the most accurate predictions among all scenarios (R = 0.968–0.988 and RMSE = 1.274–1.403 MJ/m2⋅d). Also, Scenario #3 stood in the next rank of accuracy for predicting the solar radiation in both validating stations. The AD-RF model was the best predictive, followed by AD-RS and AD-LWLR. Hence, this study recommends new effective methods to predict GSR in semi-arid regions.
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| 3. |
- Tao, Hai, et al.
(author)
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Groundwater level prediction using machine learning models: A comprehensive review
- 2022
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In: Neurocomputing. - : Elsevier. - 0925-2312 .- 1872-8286. ; 489, s. 271-308
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Research review (peer-reviewed)abstract
- Developing accurate soft computing methods for groundwater level (GWL) forecasting is essential for enhancing the planning and management of water resources. Over the past two decades, significant progress has been made in GWL prediction using machine learning (ML) models. Several review articles have been published, reporting the advances in this field up to 2018. However, the existing review articles do not cover several aspects of GWL simulations using ML, which are significant for scientists and practitioners working in hydrology and water resource management. The current review article aims to provide a clear understanding of the state-of-the-art ML models implemented for GWL modeling and the milestones achieved in this domain. The review includes all of the types of ML models employed for GWL modeling from 2008 to 2020 (138 articles) and summarizes the details of the reviewed papers, including the types of models, data span, time scale, input and output parameters, performance criteria used, and the best models identified. Furthermore, recommendations for possible future research directions to improve the accuracy of GWL prediction models and enhance the related knowledge are outlined.
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