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Rainfall Monthly Prediction Based on Artificial Neural Network: A Case Study in Tenggarong Station, East Kalimantan - Indonesia

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Date
2015-08-30
Author
Mislan, Mislan
Haviluddin, Haviluddin
Hardwinarto, Sigit
Sumaryono, Sumaryono
Aipassa, Marlon I.
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Abstract
The accuracy of forecasting rainfall is very important due to the current world climate change. Afterwards, to get an accurate forecasting of rainfall, this paper applied an Artificial Neural Network (ANN) with the Backpropagation Neural Network (BPNN) algorithm. In this experiment, the rainfall data were tested using two-hidden layers of BPNN architectures with three different epochs which were [2-50-10-1, epoch 500]; [2-50-20-1, with epochs 1000 and 1500]. The mean square error (MSE) is employed to measure the performance of the classification task. The experimental results showed that the architecture [2-50-20- 1, epoch 1000] produced a good result with the value of MSE was 0.00096341. Furthermore, BPNN algorithm has provided a good model to predict rainfall in Tenggarong, East Kalimantan - Indonesia.
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http://repository.unmul.ac.id/handle/123456789/3606
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Repository Universitas Mulawarman copyright ©   LP3M Universitas Mulawarman
Jalan Kuaro Kotak Pos 1068
Telp. (0541) 741118
Fax. (0541) 747479 - 732870
Samarinda 75119, Kalimantan Timur, Indonesia
Contact Us | Send Feedback