Prediksi Kejadian Banjir dengan Ensemble Machine Learning menggunakan BP-NN dan SVM

Flood Prediction with Ensemble Machine Learning using BP-NN and SVM

*Ike Fitriyaningsih -  Program Studi D3 Teknik Informatika, Fakultas Teknik Informatika dan Elektro, Institut Teknologi Del, Indonesia
Yuniarta Basani -  Program Studi D3 Teknik Informatika, Fakultas Teknik Informatika dan Elektro, Institut Teknologi Del, Indonesia
Received: 13 Nov 2018; Revised: 23 Jul 2019; Accepted: 29 Jul 2019; Published: 31 Jul 2019; Available online: 4 Aug 2019.
Open Access Copyright (c) 2019 Jurnal Teknologi dan Sistem Komputer
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Language: ID
Statistics: 239
This study aims to examine the prediction of rainfall and river water debit using the Back Propagation Neural Network (BP-NN) method. Prediction results are classified using the Support Vector Machine (SVM) method to predict flooding. The parameters used to predict rainfall with BP-NN are minimum, maximum and average temperature, average relative humidity, sunshine duration, and average wind speed. The debit of Ular Pulau Tagor river is predicted by BP-NN. BPNN and SVM modeling using software R. Daily climate data from 2015-2017 were taken from three stations, namely Sampali climatology station, Kualanamu meteorological station, and Tuntung geophysics station. Prediction of river water debit is for 6 days and 30 days in the future. The best dataset is a 6 day prediction with a combination of 60% training and 40% testing. Flood prediction accuraty with SVM was 100% in predicting flood events for the next 6 days.
ensamble machine learning; flood prediction, BP-NN and SVM; rainfall prediction; river water debit prediction

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