Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

Sofian, Ian Mochamad; Affandi, Azhar; Iskandar, Iskhaq; Apriani, Yosi

doi:10.26555/ijain.v4i2.208

Cited by 16 publications

(6 citation statements)

References 19 publications

(22 reference statements)

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“…Therefore, the determination of the best network model using the variables R, MSE, RMSE, and MAE was in accordance with the training and testing data from this study. The same thing had been conducted by Haviluddin and Tahyudin [37] with RBFNN for the prediction of internet network data traffic in East Kalimantan, and Sofian et al [28] for the prediction of monthly rainfall in South Sumatra.…”

Section: A Modeling Methane Emissions With Rbfnnmentioning

confidence: 82%

Modeling Methane Emission of Wastewater Anaerobic Pond at Palm Oil Mill Using Radial Basis Function Neural Network

Putro¹,

Budianta²,

Rohendi³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Plant-based industries such as palm oil mills will generate wastewater rich in organic matter. Palm oil mill effluent (POME) treatment in Indonesia is still dominant with conventional methods without the capture of methane. This system does not know the value of methane emitted into the atmosphere. Measurement and testing of biomethane from anaerobic ponds of palm oil mills are relatively difficult because gas material is rapidly changing. An alternative methodology that is accurate through modeling with a radial basis function neural network (RBFNN) with abiotic variable input. The aim of this research is to find out an anaerobic pond methane emission model of POME and simulation to find out the dynamics of methane emissions. Methane emission data is measured by a TGS2611 methane gas sensor CH 4-meter system and using closed static chambers. A sampling of wastewater and methane gas was conducted in October-November 2018. The results showed that the methane gas emission model was obtained in the AP with RBFNN. The best RBFNN model had a 5-5-3 network architecture, spread 0.11 and error-goals 0.0005, R 0.940652 and MSE 0.003166. The reliability of RBFNN in determining models with non-linear field data variables was quite good, which was influenced by the number of data patterns, types and accuracy of the variables, network architecture, and the ANN model used. The simulation and prediction of methane emissions in the lowest-moderate-highest variable value scenario found that the COD-R and VS-R variables greatly affected the anaerobic pond WWTP emissions of multiple feeding systems. Even so, inlet wastewater temperature and rainfall variables had not significantly affected methane gas emissions, because the temperature was in a mesophilic range (30-40 o C) and the effect of rainfall would depend mainly on the high-low levels of organic matter (COD and VS).

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Section: A Modeling Methane Emissions With Rbfnnmentioning

confidence: 82%

Modeling Methane Emission of Wastewater Anaerobic Pond at Palm Oil Mill Using Radial Basis Function Neural Network

Putro¹,

Budianta²,

Rohendi³

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…In this stage, the research discussion will continue to build a gold price prediction network pattern. The pattern network consists of input layer neurons, 1 neuron in the hidden layer, and 1 neuron in the output layer [29]. The network pattern that is formed is shown in Figure 2.…”

Section: Prediction Network Patternsmentioning

confidence: 99%

Implementation multiple linear regresion in neural network predict gold price

Yanto

Sanjaya

Yulasmi

et al. 2021

IJEECS

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<p>The movement of gold prices in the previous period was crucial for investors. However, fluctuations in gold price movements always occur. The problem in this study is how to apply multiple linear regression (MRL) in predicting artificial neural networks (ANN) of gold prices. MRL is mathematical calculation technique used to measure the correlation between variables. The results of the MRL analysis ensure that the network pattern that is formed can provide precise and accurate prediction results. In addition, this study aims to develop a predictive pattern model that already exists. The results of the correlation test obtained by MRL provide a correlation of 62% so that the test results are said to have a significant effect on gold price movements. Then the prediction results generated using an ANN has a mean squared error (MSE) value of 0.004264%. The benefits obtained in this study provide an overview of the gold price prediction pattern model by conducting learning and approaches in testing the accuracy of the use of predictor variables.</p>

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“…System design is a diagram-making system which uses the Unified Modeling Language (UML) with Object-Oriented Programming (OOP). System design includes Business Process, Usecase Diagram, and Entity Relationship Diagram (ERD) [13].…”

Section: B Classification Testingmentioning

confidence: 99%

Implementation of Backpropagation Neural Network Method in Classification System of Timeliness of Graduation

Nurdiansyah¹,

Media²,

Hizam³

2019

Proceedings of the International Conference of Science and Technology for the Internet of Things

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Information Systems is one of the study programs at the University of Jember which was established since 2009. To earn a degree of bachelor, students must pass 144 credits of 4-5 years study period. Method for classifying using an artificial neural network of Backpropagation. Attribute used for classification is 9 attributes, namely Grade Point Average (GPA) from 1 to 6 semester, the number of credits taken, the last semester taking Student Study Service (KKN) and Internship (PKL). Class used for the classification is the timeliness of graduation. The implementation of this classification method is done by learning rate 0.1, 0.3, 0.5, 0.7, and 0.9 with the iteration limit of 1,000, 2,000, and 3,000. The highest accuracy is 98.82% for the 2000 and 3000, each with learning rate = 0.7 and 0.9 for the 2000 th iteration and learning rate = 0.5, 0.7 and 0, 9 for the 3000 th iteration.

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Monthly rainfall prediction based on artificial neural networks with backpropagation and radial basis function

Cited by 16 publications

References 19 publications

Modeling Methane Emission of Wastewater Anaerobic Pond at Palm Oil Mill Using Radial Basis Function Neural Network

Modeling Methane Emission of Wastewater Anaerobic Pond at Palm Oil Mill Using Radial Basis Function Neural Network

Implementation multiple linear regresion in neural network predict gold price

Implementation of Backpropagation Neural Network Method in Classification System of Timeliness of Graduation

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