BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA
Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming,...
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Universitas Pattimura
2024-05-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11917 |
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| author | Suci Astutik Nur Silviyah Rahmi Diego Irsandy Fang You Dwi Ayu Shalu Saniyawati Fidia Raaihatul Mashfia Evelin Dewi Lusiana Intan Fadhila Risda Mohammad Hilmi Susanto |
| author_facet | Suci Astutik Nur Silviyah Rahmi Diego Irsandy Fang You Dwi Ayu Shalu Saniyawati Fidia Raaihatul Mashfia Evelin Dewi Lusiana Intan Fadhila Risda Mohammad Hilmi Susanto |
| author_sort | Suci Astutik |
| collection | DOAJ |
| description | Rainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains. |
| format | Article |
| id | doaj-art-df8d27a37727480fa54505aa33b220cb |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-05-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-df8d27a37727480fa54505aa33b220cb2025-08-20T03:37:31ZengUniversitas PattimuraBarekeng1978-72272615-30172024-05-011821105111610.30598/barekengvol18iss2pp1105-111611917BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVASuci Astutik0Nur Silviyah Rahmi1Diego Irsandy2Fang You Dwi Ayu Shalu Saniyawati3Fidia Raaihatul Mashfia4Evelin Dewi Lusiana5Intan Fadhila Risda6Mohammad Hilmi Susanto7Department of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Mathematics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaDepartment of Statistics, Faculty of Mathematics and Natural Science, Universitas Brawijaya, IndonesiaRainfall is an important parameter in meteorology and hydrology, and it measures the amount of rain that falls from the atmosphere to the ground surface in liquid form. However, in the process of measuring rainfall, changes in the rainfall cycle sometimes occur due to climate change, global warming, and other factors. Therefore, this research aims to model daily rainfall using the Bayesian Neural Network (BNN) approach, combining the Bayesian Method and Artificial Neural Network (ANN). ANN is suitable for rainfall models that have intermittent characteristics. Meanwhile, the Bayesian method provides advantages in producing model parameter inferences that provide uncertainty measurements in predictions. BNN is expected to deliver better daily rainfall predictions than ANN. This research used daily rainfall data in East Jawa, and the results show that the Bayesian Neural Network produces better rainfall predictions when describing rainfall in East Java. These predictions will be very useful for the government and the people of East Java province to prevent flooding. Also, with rainfall predictions, people will know more about what crops should be planted during the rains.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11917mcmcbnndaily rainfallprediction |
| spellingShingle | Suci Astutik Nur Silviyah Rahmi Diego Irsandy Fang You Dwi Ayu Shalu Saniyawati Fidia Raaihatul Mashfia Evelin Dewi Lusiana Intan Fadhila Risda Mohammad Hilmi Susanto BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA Barekeng mcmc bnn daily rainfall prediction |
| title | BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA |
| title_full | BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA |
| title_fullStr | BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA |
| title_full_unstemmed | BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA |
| title_short | BAYESIAN NEURAL NETWORK RAINFALL MODELLING: A CASE STUDY IN EAST JAVA |
| title_sort | bayesian neural network rainfall modelling a case study in east java |
| topic | mcmc bnn daily rainfall prediction |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/11917 |
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