E3S Web Conf.
Volume 73, 2018The 3rd International Conference on Energy, Environmental and Information System (ICENIS 2018)
|Number of page(s)||5|
|Section||Health, Safety and Environment Information Systems|
|Published online||21 December 2018|
Rainfall Forecast of Merauke Using Autoregressive Integrated Moving Average Model
1 Department of Chemistry Education, Faculty of Teacher Training and Education, Musamus University, Merauke - Indonesia
2 Department of Mathematics Education, Faculty of Teacher Training and Education, Musamus University, Merauke - Indonesia
3 Department of Mathematics Education, Faculty of Teacher Training and Education, Musamus University, Merauke - Indonesia
* Corresponding author: firstname.lastname@example.org
Climate is an important element for human life, one of them is to agriculture sector. Global climate change leads to increased frequency and extreme climatic intensity such as storms, floods, and droughts. Rainfall is climate factor that causes the failure of harvest in Merauke. Therefore, rainfall forecast information is very useful in anticipating the occurrence of extreme events that can lead to crop failure. The purpose of this research is to model rainfall using autoregressive integrated moving average (ARIMA) model. The ARIMA model can be used to predict future events using a set of past data, including predicting rainfall. This research was conducted by collecting secondary data from Agency of Meteorology, Climatology, and Geophysics (BMKG) from 2005 until 2017, then the data was analyzed using R.3.4.2. software. The analysis result showed that ARIMA model (2.0,2) as the right model to predict rainfall in Merauke. The result of forecasting based on ARIMA model (2.0,2) for one period ahead is 179 mm of average rainfall, 46 mm of minimum rainfall, and 295 mm of maximum rainfall. Thus it can be concluded that the intensity of rainfall in Merauke has decreased and there was a seasonal shift from the previous period.
Key words: Climate / crop failure / rainfall / Merauke / ARIMA model
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.