Rainfall Forecasting Using Backpropagation Neural Network

Sihananto, Andreas Nugroho; Mahmudy, Wayan Firdaus

doi:10.25126/jitecs.2017229

Cited by 8 publications

(12 citation statements)

References 9 publications

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“…The test parameters used are 28 hidden layer nodes on NN, 22 hidden layer nodes on RNN, epoch 500 and error 0,0001. Learning rate testing starts from 0,1 to 0,9 [1]. The purpose of this test is to find the best learning rate with the smallest MSE value.…”

Section: Figure 5 Hidden Layer Node Test Resultsmentioning

confidence: 99%

“…Neural Network (NN) is a model of information inspired by the biological nervous system. NN have been successfully implemented in several studies for prediction and classification [1], [7], [8]. NN gets knowledge through several learning processes.…”

Section: Neural Networkmentioning

confidence: 99%

“…The Rice prediction method has been done by several methods. The parameters used are paddy area, paddy standard area, harvested area, planting area, production, raindrop, rainy day, stem borer, rat, brown planthopper, blast, golden snail, leaf blight bacteria, fake white pest and productivity level [1] [2].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required. The predictions are carried out to find out the annual yield of rice in an area. Weather factors greatly affect production results so that in this study using weather parameters as input parameters. The Input Parameters are used in the Recurrent Neural Network algorithm with the Backpropagation learning process. The results are compared with Neural Networks with Backpropagation learning to find out the most effective method. In this study, the Recurrent Neural Network has better prediction results compared to a Neural Network. Based on the computational experiments, it is found that the Recurrent Neural Network obtained a Means Square Error of 0.000878 and a Mean Absolute Percentage Error of 10,8832%, while the Neural Network obtained a Means Square Error of 0.00104 and a Mean Absolute Percentage Error of 10,3804.

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Section: Figure 5 Hidden Layer Node Test Resultsmentioning

confidence: 99%

Section: Neural Networkmentioning

confidence: 99%

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Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Hamdianah

Mahmudy

Widaryanto

2021

JITeCS

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“…While the last two networks consist of an input layer with ten input parameters, two hidden layers with nine neurons in the first hidden layer and one neuron in the second layer. Meanwhile [18], to predict the level of rainfall is very complex with a large number of parameters, using an alternative approach based on time-series models. One of the algorithms that is widely used to predict the future is Neural Network Backpropagation.…”

Section: Literature Reviewmentioning

confidence: 99%

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Shofura

Suryani

Salma

et al. 2021

ijoict

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Current weather-related research only focuses on weather prediction based on raw data and the factors used are generally 4 factors: average temperature, solar radiation, air pressure, and wind. In this research, monthly weather prediction is done using 5 factors where the additional factor used is rainfall in the previous time. In contrast to previous prediction research, the prediction process carried out in this study emphasizes the modeling of training data according to the desired prediction model.. These two things distinguish this research from previous studies. The prediction model used in this study is a classification-based prediction model that is the Artificial Neural Network (ANN) method combined with the backpropagation algorithm for calculating the weight of the ANN network. The data used are meteorological data from 2010 to 2018 in the Bogor area, where data from 2010 to 2016 are used as training data, and data from 2017 to 2018 are used as test data. The results of this study indicate that the design of the model with the use of data for 6 years with feature data of 5 factors has an accuracy rate of 83.33%.

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“…Prediction of traffic was done by using an RBFNN with different statistical analyses for measuring the predicted results by Haviluddin and Tahyudin 17 . Sihananto and Mahmudy 18 used weight initialization with a back‐propagation neural network (BPNN) for forecasting rainfall in different areas.…”

Section: Introductionmentioning

confidence: 99%

Power prediction of waste heat recovery system for a cement plant using back propagation neural network and its thermodynamic modeling

et al. 2021

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This work targets on the prediction of power produced by a waste heat recovery (WHR) system for a cement plant using the back-propagation neural network (BPNN) and comparing its results with the actual cycle. A regressionbased predictive model is developed to compare it with an actual WHR cycle that is analyzed thermodynamically. The predictive model is trained using the following parameters as input features: turbine inlet steam, pressure, temperature, and mass flow rate of both stages (i.e., high-pressure (HP) and lowpressure (LP) stages). The mean square error for the validation dataset is found to be 0.283 with 10 neurons in the hidden layer. Moreover, the significance of each parameter on the predicted power investigated shows that the HP stage parameters tend to have more impact on the power generated. The other half of the study comprises energy analysis of the power plant and a detailed parametric analysis to review the impact of each parameter on the power produced and thermal efficiency. The calculated thermal efficiency and generated power of the system are found to be 19.75% and 10.06 MW, respectively. Lastly, from the comparative study of the predictive model and the actual WHR simulated cycle, it is concluded that data science can be used as an alternative to thermodynamic modeling to avoid hefty calculations.

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Rainfall Forecasting Using Backpropagation Neural Network

Cited by 8 publications

References 9 publications

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java

The Effect of Number of Factors and Data on Monthly Weather Classification Performance Using Artificial Neural Networks

Power prediction of waste heat recovery system for a cement plant using back propagation neural network and its thermodynamic modeling

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