A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment

Başakın, Eyyup Ensar; Ekmekçioğlu, Ömer; Çıtakoğlu, Hatice; Özger, Mehmet

doi:10.1007/s00521-021-06424-6

Cited by 60 publications

(15 citation statements)

References 88 publications

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“…Moreover, the statistical significance of the acquired data was examined using the Kruskal–Wallis test in this study, additionally to an analysis of whether the predicted and observed or LPI distributions, were consistent (Başakın et al, 2021 ; Citakoglu, 2021 ). Wherewith H 0 denotes a hypothesis based on the statistically significant difference between mean predicted and observed efficiency score.…”

Section: Findings and Discussionmentioning

confidence: 99%

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An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

2023

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In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

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Section: Findings and Discussionmentioning

confidence: 99%

“…The mean absolute error (MAE) (8), the root mean square error (RMSE) (9), and Nash − Sutcliffe efficiency coefficient (NSE) (10) (Başakın et al, 2021 ) along with the Kruskal‐Wallis test at 95% confidence interval will be used to measure the performance of model. …”

Section: Methods and Applicationmentioning

confidence: 99%

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

2023

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“…Pourtousi, Khalijian, Ghanizadeh, Babanezhad, Nakhjiri et al, 2021), different dimensions of learners (Bachtiar, Sulistyo, Cooper, & Kamei, 2017), prediction of various diseases such as cancer (Kalaiselvi, & Nasira, 2014) or even diabetes (Kirisci, Yılmaz, & Saka, 2019), weather forecasting (Tektaş, 2010), wind speed prediction (Liu, Tian, & Li, 2015), etc. It is also worth mentioning that for predicting the wind speed, other models, such as support vector machines and Gaussian process regression, can be applied (Başakın, Ekmekcioğlu, Çıtakoğlu, & Özger, 2021). However, using ANFIS seems to be an efficient computation approach.…”

Section: Anfis Methodsmentioning

confidence: 99%

Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature

Zhang¹,

Band²,

Ardabili³

et al. 2022

Preprint

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In this research, dew point temperature (DPT) is simulated using the data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized as a data-driven technique to forecast this parameter at Tabriz in East Azerbaijan. Various input patterns, namely T min, T max, and T mean, are utilized for training the architecture whilst DPT is the model's output. The findings indicate that, in general, ANFIS method is capable of identifying data patterns with a high degree of accuracy. However, the approach demonstrates that processing time and computer resources may substantially increase by adding additional functions. Based on the results, the number of iterations and computing resources might change dramatically if new functionalities are included. As a result, tuning parameters have to be optimized inside the method framework. The findings demonstrate a high agreement between results by the data-driven technique (machine learning method) and the observed data. Using this prediction toolkit, DPT can be adequately forecasted solely based on the temperature distribution of Tabriz. This kind of modeling is extremely promising for predicting DPT at various sites. Besides, this study thoroughly compares the Bilayered Neural Network (BNN) and ANFIS models on various scales. Whilst the ANFIS model is extremely stable for almost all numbers of membership functions, the BNN model is highly sensitive to this scale factor to predict DPT.

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“…In this study, four statistical error parameters such as mean absolute error (MAE), root mean square error (RMSE), Nash−Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R) are used for the assessment of the accuracy of the models in forecasting the observed output variable. MAE, RMSE, NSE, and R parameters, respectively, are expressed as follows (Başakın et al 2021;Citakoglu 2021):…”

Section: Statistical Parametersmentioning

confidence: 99%

Machine Learning Approaches for One-Day Ahead Soil Temperature Forecasting

Bilgili

Ünal

Şekertekin

et al. 2023

J Agr Sci-Tarim Bili

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Present study investigates the capabilities of six distinct machine learning techniques such as ANFIS network with fuzzy c-means (ANFIS-FCM), grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), feed-forward neural network (FNN), Elman neural network (ENN), and long short-term memory (LSTM) neural network in one-day ahead soil temperature (ST) forecasting. For this aim, daily ST data gathered at three different depths of 5 cm, 50 cm, and 100 cm from the Sivas meteorological observation station in the Central Anatolia Region of Turkey was used as training and testing datasets. Forecasting values of the machine learning models were compared with actual data by assessing with respect to four statistic metrics such as the mean absolute error, root mean square error (RMSE), Nash−Sutcliffe efficiency coefficient, and correlation coefficient (R). The results showed that the ANFIS-FCM, ANFIS-GP, ANFIS-SC, ENN, FNN and LSTM models presented satisfactory performance in modeling daily ST at all depths, with RMSE values ranging 0.0637-1.3276, 0.0634-1.3809, 0.0643-1.3280, 0.0635-1.3186, 0.0635-1.3281, and 0.0983-1.3256 °C, and R values ranging 0.9910-0.9999, 0.9903-0.9999, 0.9910-0.9999, 0.9911-0.9999, 0.9910-0.9999 and 0.9910-0.9998 °C, respectively.

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A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment

Cited by 60 publications

References 88 publications

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

Integration of neural network and fuzzy logic decision making compared with bilayered neural network in the simulation of daily dew point temperature

Machine Learning Approaches for One-Day Ahead Soil Temperature Forecasting

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