Prediction of Rainfall using Simplified Deep Learning based Extreme Learning Machines

• Main Authors: Imam Cholissodin, Sutrisno Sutrisno
• Format: Article
• Language: English
• Published: University of Brawijaya 2018-11-01
• Series: JITeCS (Journal of Information Technology and Computer Science)
• Online Access: http://jitecs.ub.ac.id/index.php/jitecs/article/view/58

Prediction of rainfall is needed by every farmer to determine the planting period or for an institution, eg agriculture ministry in the form of plant calendars. BMKG is one of the national agency in Indonesia that doing research in the field of meteorology, climatology, and geophysics in Indonesia using several methods in predicting rainfall. However, the accuracy of predicted results from BMKG methods is still less than optimal, causing the accuracy of the planting calendar to only reach 50% for the entire territory of Indonesia. The reason is because of the dynamics of atmospheric patterns (such as sea-level temperatures and tropical cyclones) in Indonesia are uncertain and there are weaknesses in each method used by BMKG. Another popular method used for rainfall prediction is the Deep Learning (DL) and Extreme Learning Machine (ELM) included in the Neural Network (NN). ELM has a simpler structure, and non-linear approach capability and better convergence speed from Back Propagation (BP). Unfortunately, Deep Learning method is very complex, if not using the process of simplification, and can be said more complex than the BP. In this study, the prediction system was made using ELM-based Simplified Deep Learning to determine the exact regression equation model according to the number of layers in the hidden node. It is expected that the results of this study will be able to form optimal prediction model. Keywords: prediction, rainfall, ELM, simplified deep learning