Research Article
Implementation Extreme Learning Machine for Rainfall Forecasting
@INPROCEEDINGS{10.4108/eai.2-8-2019.2290495, author={Laksmita Puspaningrum and Ayundyah Kesumawati}, title={Implementation Extreme Learning Machine for Rainfall Forecasting}, proceedings={Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia}, publisher={EAI}, proceedings_a={ICSA}, year={2020}, month={1}, keywords={rainfall forecasting extreme learning machine neural network garch}, doi={10.4108/eai.2-8-2019.2290495} }- Laksmita Puspaningrum
Ayundyah Kesumawati
Year: 2020
Implementation Extreme Learning Machine for Rainfall Forecasting
ICSA
EAI
DOI: 10.4108/eai.2-8-2019.2290495
Abstract
Indonesia is one of country that have a large number of rainfall days in a year. So, make any sense that forecasting is important to get any strategies to overcome the problem of erratic rainfall. There are a lot of method that can conduct forecast rainfall, the new one is Extreme Machine Learning. Extreme Learning Machine (ELM) is a new learning method of artificial neural networks. ELM is an easy-to use and effective learning algorithm of single-hidden layer feed-forward neural networks (SLFNs). Therefore, ELM has the advantages of fast learning speed and good generalization performance. This research conducted by using rainfall data in Sleman city to get forecasting in one year. It found that the ELM method has a smaller value compared to the GARCH method for all six rainfall station stations in Sleman region.
