Identify Flood Disaster and Mitigation Using Neural Network Learning Vector Quantization in Malang City
- DOI
- 10.2991/icovet-18.2019.47How to use a DOI?
- Abstract
Flood is the most common disaster in Indonesia and certainly harmful to society in the form of material or psychical. Therefore, it’s necessary to identify the potential and flood mitigation earlier to reduce the potential losses suffered by the society after the occurrence of disaster. This is difficult to do with conventional methods so that in this research proposed "Neural Network Learning Vector Quantization as Identification Method of Potential and Mitigation of Flood Disaster". With this algorithm specified four nodes input layer, one hiden layer with two neurons and two output layers where four nodes input layer are elevation, drainage, rainfall and flood events are derived from data of BPS Malang, BMKG Karangploso, and data of BPBN. Data processing and testing will generate two outputs, they are identification of flooding potential area and no flooding potential area in every villages in Malang. The test results by using confution matrix showed the accuracy value at 95.34%, sensitivity value at 100%, specification value at 95.29%, and error rate at 4.68% on 1710 dataset that composed of 70% training data and 30% testing data with learning rate at 0.1, decrement learning rate at 0.01, maximum epoch at 10 and minimum epoch at 0.0000001.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Muhammad Ashar AU - Kartika Devi Suraningtyas AU - I Made Wirawan AU - Farhan Afzal PY - 2019/01 DA - 2019/01 TI - Identify Flood Disaster and Mitigation Using Neural Network Learning Vector Quantization in Malang City BT - Proceedings of the 2nd International Conference on Vocational Education and Training (ICOVET 2018) PB - Atlantis Press SP - 184 EP - 187 SN - 2352-5398 UR - https://doi.org/10.2991/icovet-18.2019.47 DO - 10.2991/icovet-18.2019.47 ID - Ashar2019/01 ER -