Cahyadi, Tedy Agung and Jayadianti, Herlina and Amri, Nur Ali and Pitayandanu, Muhammad Fathurrahman and Ashar, Abu (2020) Monthly Prediction of Rainfall in Nickel Mine Area with Artificial Neural Network. 3rd International Conference on Earth Science, Mineral, and Energy, - (-). 060008-060008. ISSN -
|
Text
1. 2020_Nur AA-Tedy AC-Herlina_JADI_PI_Monthly prediction-2.pdf Download (6MB) | Preview |
Abstract
Rainfall prediction in the mining area was needed to assist the process of mine drainage, and monitoring the availability of water in the reservoir, which is a source of hydroelectric power. This research wants to know how good is ANN with weather and time series as input parameters in some architectures. Various ANN architectures were examined in this study to make predictions with monthly rainfall parameters t-3, t-2 and t-1. It included supporting parameters such as average exposure time, humidity, temperature, wind speed, and finally predicts rainfall in the month of occurrence. The ANN architecture contains a hidden layer which was examined by the optimal number of neurons and epochs. Hidden neurons were tried from seven to fourteen. The results of experiment showed that the architecture [7-8-1, 500 epochs] concluded that ANN gave good results of MSE which were 0.05865 for training and 0.08725 for testing. Furthermore, the ANN algorithm has provided to predict rainfall with a good model. Keyword: Rainfall, Prediction, ANN, Architecture
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Rainfall, Prediction, ANN, Architecture |
| Subjects: | Z Bibliography. Library Science. Information Resources > ZA Information resources |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Engineering Sciences |
| Depositing User: | Dr. ST. MT Nur Ali Amri |
| Date Deposited: | 20 Dec 2021 14:12 |
| Last Modified: | 20 Dec 2021 14:12 |
| URI: | http://eprints.upnyk.ac.id/id/eprint/27329 |
Actions (login required)
 |
View Item |