S. D., Permai and M., Ohyver and M. K. B. M., Aziz (2021) Daily rainfall modeling using neural network. In: Journal of Physics: Conference Series, Simposium Kebangsaan Sains Matematik ke-28 (SKSM28) , 28-29 July 2021 , Kuantan, Pahang, Malaysia. pp. 1-11., 1988 (012040). ISSN 1742-6588
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Abstract
In the early 2020, Indonesia experienced flooding in several areas. This disaster caused a lot of damage and losses. One of the causes of flooding in Indonesia is due to high rainfall. This was not anticipated beforehand so there was a flood. Therefore, research on rainfall in Indonesia is very important to anticipate floods. If it is predicted that rainfall is very high and conditions do not allow it to accommodate, the government can prepare watersheds so that rainwater can flow and not be trapped. In this research, the rainfall data were obtained from Meteorological, Climatological, and Geophysical Agency (BMKG Indonesia), then the analysis of rainfall data in Indonesia was carried out. There are several statistical methods that can be used. There are ARIMA and Neural Network. In this research, the results of ARIMA model are used as input variables in the Neural Network model. Then there are several numbers of hidden layer in the Neural Network model that are compared. The results of ARIMA model and Neural Network model showed that Neural Network model is better than ARIMA model, because the mean square error (MSE) value of Neural Network model is smaller than ARIMA model.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | ARIMA modeling; Daily rainfall model; Hidden layers; Indonesia; Input variables; Neural network model; Rainfall data |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics |
| Faculty/Division: | Center for Mathematical Science |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 07 Nov 2022 04:57 |
| Last Modified: | 07 Nov 2022 04:57 |
| URI: | http://umpir.ump.edu.my/id/eprint/35200 |
| Download Statistic: | View Download Statistics |
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