A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran

Jahani, Babak; Mohammadi, Babak

doi:10.1007/s00704-018-2666-3

Cited by 95 publications

(27 citation statements)

References 97 publications

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“…In this algorithm, the search is commenced in an initial population of the strains and the unique strains are evaluated in each repetition corresponding to an efficiency condition and assigned a proportion value. For complementary information, refer to the article by Jaramillo et al [41].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Aghelpour

Mohammadi

Biazar

et al. 2020

IJGI

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Precipitation deficit can affect different natural resources such as water, soil, rivers and plants, and cause meteorological, hydrological and agricultural droughts. Multivariate drought indexes can theoretically show the severity and weakness of various drought types simultaneously. This study introduces an approach for forecasting joint deficit index (JDI) and multivariate standardized precipitation index (MSPI) by using machine–learning methods and entropy theory. JDI and MSPI were calculated for the 1–12 months’ time window (JDI1–12 and MSPI1–12), using monthly precipitation data. The methods implemented for forecasting are group method of data handling (GMDH), generalized regression neural network (GRNN), least squared support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS) and ANFIS optimized with three heuristic optimization algorithms, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO) as meta-innovative methods (ANFIS-DE, ANFIS-GA and ANFIS-PSO). Monthly precipitation, monthly temperature and previous amounts of the index’s values were used as inputs to the models. Data from 10 synoptic stations situated in the widest climatic zone of Iran (extra arid-cold climate) were employed. Optimal model inputs were selected by gamma test and entropy theory. The evaluation results, which were given using mean absolute error (MAE), root mean squared error (RMSE) and Willmott index (WI), show that the machine learning and meta-innovative models can present acceptable forecasts of general drought’s conditions. The algorithms DE, GA and PSO, could improve the ANFIS’s performance by 39.4%, 38.7% and 22.6%, respectively. Among all the applied models, the GMDH shows the best forecasting accuracy with MAE = 0.280, RMSE = 0.374 and WI = 0.955. In addition, the models could forecast MSPI better than JDI in the majority of cases (stations). Among the two methods used to select the optimal inputs, it is difficult to select one as a better input selector, but according to the results, more attention can be paid to entropy theory in drought studies.

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Aghelpour

Mohammadi

Biazar

et al. 2020

IJGI

Self Cite

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“…The equation that minimizes the error function is given in equation (6). α * a and α a are the LaGrange multipliers and are referred to as the support vectors if the training vector has a value other than zero.…”

Section: Mffnn Algorithmmentioning

confidence: 99%

“…In addition, radiation values cannot be measured in an accurate way in most countries because the measurements can only be made in certain areas. For this reason, experimental, statistical, and Artificial Intelligence-(AI-) based estimation methods were developed to calculate the value of SR worldwide [6][7][8]. ML algorithms, which are a subfield of AI, are one of the most common methods used in estimation studies.…”

Section: Introductionmentioning

confidence: 99%

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

Guher

Taşdemir

Yanıktepe

2020

International Journal of Photoenergy

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The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), K -Nearest Neighbors ( K -NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, R 2 , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

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“…The artificial neural system (ANN) is widely drawn in for numerical prescience and grouping (Aggarwal, Goel, & Singh, 2012;Jahani & Mohammadi, 2019;Kia et al, 2012;Moazenzadeh & Mohammadi, 2019). It is manufactured with quantities of handling components and includes three essential layers, for example, the information layer, hidden layer, and output layer correspondingly.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Assessment of infiltration models developed using soft computing techniques

Sihag

Kumar

Singh³

2020

Geology, Ecology, and Landscapes

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In this study, predicting ability of support vector machines (SVM), Gaussian process (GP), artificial neural network (ANN), and Random forests (RF) based regression approaches was tested on the infiltration data of soil samples having different compositions of sand, silt, clay, and fly ash. In addition to this, their performances were compared with the Kostiakov model (KM) and Philip's model (PM). Dataset containing a total of 392 observations was collected from the experimental measurements of soil infiltration rate on different soil samples. Out of the total dataset, 272 recordings were randomly selected for training and the residual 120 observations were selected for validation of the developed models. Standard statistical parameters were used to measure the predicting ability of various developed models. The result suggests that the best performance could be achieved by Polynomial kernel function-based GP regression (GP_Poly) with coefficient of correlation values as 0.9824, 0.9863, Bias values as 0.0006, −2.3542, root-mean-square error values as 47.7336, 40.3026, and Nash Sutcliffe model efficiency values as 0.9655, 0.9727 using training and testing dataset, respectively. Furthermore, time is found as the most influencing input variable for predicting the infiltration rate when GP_Poly-based model is used to predict the infiltration rate.

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A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran

Cited by 95 publications

References 97 publications

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

A Theoretical Approach for Forecasting Different Types of Drought Simultaneously, Using Entropy Theory and Machine-Learning Methods

Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

Assessment of infiltration models developed using soft computing techniques

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