Proceedings of the 1st International Conference on Statistics and Analytics, ICSA 2019, 2-3 August 2019, Bogor, Indonesia

Research Article

Implementation Extreme Learning Machine for Rainfall Forecasting

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  • @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
Laksmita Puspaningrum1,*, Ayundyah Kesumawati1
  • 1: Statistic Deparment, Universitas Islam Indonesia, Yogyakarta 55584, Indonesia
*Contact email: 14611059@students.uii.ac.id

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.