Rainfall Prediction Using Backpropagation with Parameter Tuning
Rainfall is one of the important elements in the process of weather and climate. The high intensity of rainfall every year can hamper the mobility of the population and the distribution of goods, especially in the port area. Rainfall prediction is needed to handle the impacts caused by high rainfall...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2022-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2022/19/matecconf_icst2022_07003.pdf |
| _version_ | 1811178371844931584 |
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| author | Setiawan Wahyudi Barokah Asyroful Mula’ab |
| author_facet | Setiawan Wahyudi Barokah Asyroful Mula’ab |
| author_sort | Setiawan Wahyudi |
| collection | DOAJ |
| description | Rainfall is one of the important elements in the process of weather and climate. The high intensity of rainfall every year can hamper the mobility of the population and the distribution of goods, especially in the port area. Rainfall prediction is needed to handle the impacts caused by high rainfall. The data was obtained from the website dataonline.bmkg.go.id with observations made by the Tanjung Perak Surabaya Maritime Meteorological Station. The prediction method uses an artificial neural network with Backpropagation. Autocorrelation function is used to determine the number of input neurons with the best features in the Artificial Neural Network. Rainfall data is divided into two parts,: January 2008 to December 2019 used for training data and January to August 2020 for testing data. The validation technique used is 10-Fold Cross Validation. The experiment uses parameter tuning of iteration and learning rate. The training process obtained the best learning rate was 0.2 and 1000 iterations with a MSE validation score of 0.02591.Finally, the testing process has a Mean Square Error value of 0.02769 and a percentage of true rain character of 62.5%. |
| first_indexed | 2024-04-11T06:17:06Z |
| format | Article |
| id | doaj.art-1dc5b14cccce4114b970ab92bbe61d45 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-11T06:17:06Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-1dc5b14cccce4114b970ab92bbe61d452022-12-22T04:41:00ZengEDP SciencesMATEC Web of Conferences2261-236X2022-01-013720700310.1051/matecconf/202237207003matecconf_icst2022_07003Rainfall Prediction Using Backpropagation with Parameter TuningSetiawan Wahyudi0Barokah Asyroful1Mula’ab2Department of Informatics, University of Trujonoyo MaduraDepartment of Informatics, University of Trujonoyo MaduraDepartment of Informatics, University of Trujonoyo MaduraRainfall is one of the important elements in the process of weather and climate. The high intensity of rainfall every year can hamper the mobility of the population and the distribution of goods, especially in the port area. Rainfall prediction is needed to handle the impacts caused by high rainfall. The data was obtained from the website dataonline.bmkg.go.id with observations made by the Tanjung Perak Surabaya Maritime Meteorological Station. The prediction method uses an artificial neural network with Backpropagation. Autocorrelation function is used to determine the number of input neurons with the best features in the Artificial Neural Network. Rainfall data is divided into two parts,: January 2008 to December 2019 used for training data and January to August 2020 for testing data. The validation technique used is 10-Fold Cross Validation. The experiment uses parameter tuning of iteration and learning rate. The training process obtained the best learning rate was 0.2 and 1000 iterations with a MSE validation score of 0.02591.Finally, the testing process has a Mean Square Error value of 0.02769 and a percentage of true rain character of 62.5%.https://www.matec-conferences.org/articles/matecconf/pdf/2022/19/matecconf_icst2022_07003.pdfautocorrelation functionbackpropagationcross validationrainfall prediction |
| spellingShingle | Setiawan Wahyudi Barokah Asyroful Mula’ab Rainfall Prediction Using Backpropagation with Parameter Tuning MATEC Web of Conferences autocorrelation function backpropagation cross validation rainfall prediction |
| title | Rainfall Prediction Using Backpropagation with Parameter Tuning |
| title_full | Rainfall Prediction Using Backpropagation with Parameter Tuning |
| title_fullStr | Rainfall Prediction Using Backpropagation with Parameter Tuning |
| title_full_unstemmed | Rainfall Prediction Using Backpropagation with Parameter Tuning |
| title_short | Rainfall Prediction Using Backpropagation with Parameter Tuning |
| title_sort | rainfall prediction using backpropagation with parameter tuning |
| topic | autocorrelation function backpropagation cross validation rainfall prediction |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2022/19/matecconf_icst2022_07003.pdf |
| work_keys_str_mv | AT setiawanwahyudi rainfallpredictionusingbackpropagationwithparametertuning AT barokahasyroful rainfallpredictionusingbackpropagationwithparametertuning AT mulaab rainfallpredictionusingbackpropagationwithparametertuning |