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.
Skip Nav Destination
Article navigation
23 November 2021
3RD INTERNATIONAL CONFERENCE ON EARTH SCIENCE, MINERAL, AND ENERGY
25 November 2020
Yogyakarta, Indonesia
Research Article|
November 23 2021
Monthly prediction of rainfall in nickel mine area with artificial neural network
Tedy Agung Cahyadi;
Tedy Agung Cahyadi
a)
1
Mining Engineering, Faculty of Mineral Technology, Universitas Pembangunan Nasional “Veteran” Yogyakarta
, 55283 Sleman, Indonesia
a)Corresponding author: tedyagungc@upnyk.ac.id
Search for other works by this author on:
Herlina Jayadianti;
Herlina Jayadianti
b)
2
Informatics Engineering, Faculty of Industrial Engineering, Universitas Pembangunan Nasional “Veteran”
Yogyakarta, 55283 Sleman, Indonesia
Search for other works by this author on:
Nur Ali Amri;
Nur Ali Amri
c)
1
Mining Engineering, Faculty of Mineral Technology, Universitas Pembangunan Nasional “Veteran” Yogyakarta
, 55283 Sleman, Indonesia
Search for other works by this author on:
Muhammad Fathurrahman Pitayandanu;
Muhammad Fathurrahman Pitayandanu
d)
2
Informatics Engineering, Faculty of Industrial Engineering, Universitas Pembangunan Nasional “Veteran”
Yogyakarta, 55283 Sleman, Indonesia
Search for other works by this author on:
AIP Conf. Proc. 2363, 060008 (2021)
Citation
Tedy Agung Cahyadi, Herlina Jayadianti, Nur Ali Amri, Muhammad Fathurrahman Pitayandanu, Abu Ashar; Monthly prediction of rainfall in nickel mine area with artificial neural network. AIP Conf. Proc. 23 November 2021; 2363 (1): 060008. https://doi.org/10.1063/5.0061088
Download citation file:
Pay-Per-View Access
$40.00
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
29
Views
Citing articles via
Design of a 100 MW solar power plant on wetland in Bangladesh
Apu Kowsar, Sumon Chandra Debnath, et al.
Social mediated crisis communication model: A solution for social media crisis?
S. N. A. Hamid, N. Ahmad, et al.
The effect of a balanced diet on improving the quality of life in malignant neoplasms
Yu. N. Melikova, A. S. Kuryndina, et al.
Related Content
Real-time data improves rainfall prediction accuracy using artificial neural network
AIP Conf. Proc. (July 2024)
Bat algorithm and neural network for monthly streamflow prediction
AIP Conf. Proc. (November 2018)
Modeling and forecasting monthly tourist arrivals to the United States and Indonesia using ARIMA hybrids of multilayer perceptron models
AIP Conf. Proc. (January 2023)
Maximal overlap discrete wavelet transform Gaussian Process Regression for monthly crude oil price forecasting
AIP Conf. Proc. (February 2023)
Assessing distributions for monthly mean wind speed data
AIP Conference Proceedings (November 2016)