Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines

Shamshirband, Shahaboddin; Mosavi, Amir; Rabczuk, Timon; Nabipour, Narjes; Chau, Kwok Wing

doi:10.1080/19942060.2020.1773932

Cited by 81 publications

(45 citation statements)

References 24 publications

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“…Tree algorithms, such as the random forest algorithm and the gradient boosting regression tree (GBRT) algorithm, have been used to construct solar radiation prediction models with encouraging results (Sun et al, 2016;Persson et al, 2017;Fan et al, 2018;Zeng et al, 2020). In recent years, some scholars have carried out the comparative analysis of a variety of machine learning algorithms (Meenal and Selvakumar, 2018;Pang et al, 2020;Shamshirband et al, 2020), and all these works show that the ANN algorithm does not realize good prediction results but provides a direction for algorithm improvement. Some studies use deep learning techniques to predict solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

et al. 2021

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Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature, and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR), and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR, and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

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Section: Introductionmentioning

confidence: 99%

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

et al. 2021

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“…This method was also compared with the collocation method and demonstrated the DEM advantage in solving partial differential equations which are capable of using the variational format of the boundary value problem instead of the strong format. Other NN applications for analyzing mechanical problems can be found in (Najafi et al, 2018;Goswami et al, 2020;Shamshirband et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Optimizing the neural network hyperparameters utilizing genetic algorithm

Nikbakht

Anitescu

Rabczuk

2021

J. Zhejiang Univ. Sci. A

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Neural networks (NNs), as one of the most robust and efficient machine learning methods, have been commonly used in solving several problems. However, choosing proper hyperparameters (e.g. the numbers of layers and neurons in each layer) has a significant influence on the accuracy of these methods. Therefore, a considerable number of studies have been carried out to optimize the NN hyperparameters. In this study, the genetic algorithm is applied to NN to find the optimal hyperparameters. Thus, the deep energy method, which contains a deep neural network, is applied first on a Timoshenko beam and a plate with a hole. Subsequently, the numbers of hidden layers, integration points, and neurons in each layer are optimized to reach the highest accuracy to predict the stress distribution through these structures. Thus, applying the proper optimization method on NN leads to significant increase in the NN prediction accuracy after conducting the optimization in various examples.

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“…The feedforward neural network trained with the backpropagation algorithm is a well-known machine learning method. According to easy implementation, suitable performance, and inherent simplicity [24], it has been successfully applied in different fields of science [25][26][27][28]. To overcome the drawbacks of this approach, including slow convergence, time-consuming training [29], and trapping in local minima that leads to low generalizability [30], the extreme learning machine (ELM) [31] was introduced.…”

Section: Introductionmentioning

confidence: 99%

Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

et al. 2021

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Shortwave radiation density flux (SRDF) modeling can be key in estimating actual evapotranspiration in plants. SRDF is the result of the specific and scattered reflection of shortwave radiation by the underlying surface. SRDF can have profound effects on some plant biophysical processes such as photosynthesis and land surface energy budgets. Since it is the main energy source for most atmospheric phenomena, SRDF is also widely used in numerical weather forecasting. In the current study, an improved version of the extreme learning machine was developed for SRDF forecasting using the historical value of this variable. To do that, the SRDF through 1981–2019 was extracted by developing JavaScript-based coding in the Google Earth Engine. The most important lags were found using the auto-correlation function and defined fifteen input combinations to model SRDF using the improved extreme learning machine (IELM). The performance of the developed model is evaluated based on the correlation coefficient (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash–Sutcliffe efficiency (NSE). The shortwave radiation was developed for two time ahead forecasting (R = 0.986, RMSE = 21.11, MAPE = 8.68%, NSE = 0.97). Additionally, the estimation uncertainty of the developed improved extreme learning machine is quantified and compared with classical ELM and found to be the least with a value of ±3.64 compared to ±6.9 for the classical extreme learning machine. IELM not only overcomes the limitation of the classical extreme learning machine in random adjusting of bias of hidden neurons and input weights but also provides a simple matrix-based method for practical tasks so that there is no need to have any knowledge of the improved extreme learning machine to use it.

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Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines

Abstract: wing Chau (2020) Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines, Engineering

Cited by 81 publications

References 24 publications

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

Optimizing the neural network hyperparameters utilizing genetic algorithm

Prognostication of Shortwave Radiation Using an Improved No-Tuned Fast Machine Learning

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