Do quadratic and Poisson regression models help to predict monthly rainfall?

Kassem, Youssef; Gökçekuş, Hüseyin

doi:10.5004/dwt.2021.26397

Cited by 18 publications

(5 citation statements)

References 44 publications

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“…Machine learning models are used as alternative tools to describe a complex system [42]. They are utilized in a wide variety of applications in engineering and science.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…The declining gradient of the back-propagation algorithm is utilized to reduce the value of MSE between the actual and estimated output. The description of the developed model was given in Kassem and Gokcekus [42]. Figure 2 illustrates the explanation process of the proposed MFFNN method.…”

Section: (A) Multilayer Feedforward Neural Network (Mffnn)mentioning

confidence: 99%

“…[43]. It is similar to a feedforward neural network, but it contains a connection from the input and every previous layer to the layers of the following layer [42][43][44]. The favorable position of this model is that it can convey the nonlinear association without getting rid of the linear association between input and output.…”

Section: (A) Multilayer Feedforward Neural Network (Mffnn)mentioning

confidence: 99%

“…The ideal number of neurons is established on the lowest value of RMSE. The description of the developed model was given in Kassem and Gokcekus [42].…”

Section: (A) Multilayer Feedforward Neural Network (Mffnn)mentioning

confidence: 99%

“…Speed and effi-ciency are the most important advantages of RBFNN models compared to other multilayer perceptron models due to their simple structure. The description of the developed model was given in Kassem and Gokcekus [42]. Figure 4 illustrates the steps of the proposed model (RBFNN).…”

Section: (C) Radial Basis Neural Network (Rbfnn)mentioning

confidence: 99%

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Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches

Çamur

Al-Ani

2022

Energies

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The oxidation stability (OX) of the biodiesel is an essential parameter mainly during storage, which reduces the quality of the biodiesel, thus affecting the engine performance. Moreover, many factors affect oxidation stability. Therefore, determining the most significant parameter is essential for achieving accurate predictions. In this paper, an empirical equation (Poisson Regression Model (PRM)), machine learning models (Multilayer Feed-Forward Neural Network (MFFNN), Cascade Feed-forward Neural Network (CFNN), Radial Basis Neural Network (RBFNN), and Elman neural network (ENN)) with various combinations of input parameters are utilized and employed to identify the most relevant parameters for prediction of the oxidation stability of biodiesel. This study measured the physicochemical properties of 39 samples of waste frying methyl ester and their blends with various percentages of palm biodiesel and refined canola biodiesel. To this aim, 14 parameters including concentration amount of WFME (X1), PME (X2), and RCME (X3) in the mixture, kinematic viscosity (KV) at 40 °C, density at 15 °C (D), cloud point (CP), pour point (PP), the estimation value of the sum of the saturated (∑SFAMs), monounsaturated (∑MUFAMs), polyunsaturated (∑PUFAMs), degree of unsaturation (DU), long-chain saturated factor (LCSF), very-long-chain fatty acid (VLCFA), and ratio (∑MUFAMs+∑PUFAMs∑SFAMs) fatty acid composition were considered. The results demonstrated that the RBFNN model with the combination of X1, X2, X3, ∑SFAMs, ∑MUFAMs, ∑PUFAMs. VLCFA, DU, LCSF, ∑MUFAMs+∑PUFAMs∑SFAMs, KV, and D has the lowest value of root mean squared error and mean absolute error. In the end, the results demonstrated that the RBFNN model performed well and presented high accuracy in estimating the value of OX for the biodiesel samples compared to PRM, MFFNN, CFNN, and ENN.

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“…Machine learning models are used as alternative tools to describe a complex system [42]. They are utilized in a wide variety of applications in engineering and science.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: (A) Multilayer Feedforward Neural Network (Mffnn)mentioning

confidence: 99%

Section: (A) Multilayer Feedforward Neural Network (Mffnn)mentioning

confidence: 99%

“…The ideal number of neurons is established on the lowest value of RMSE. The description of the developed model was given in Kassem and Gokcekus [42].…”

Section: (A) Multilayer Feedforward Neural Network (Mffnn)mentioning

confidence: 99%

Section: (C) Radial Basis Neural Network (Rbfnn)mentioning

confidence: 99%

See 3 more Smart Citations