A novel early warning system using fuzzy multiple attribute decision making algorithm and meteorological data

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Muslikhin, Fatchul Arifin, Ponco Walipranoto, Arif Ashari

2018 Journal of Theoretical and Applied Information Technology Vol. 96 Issue 16 Article Cited by 1

Abstract

An early warning system (EWS) has the possibility to predict data accurately in the limited positions of units using sensors and additional data input. But in reality it is not easy, requires a lot of system, cross platform and field of science. This applies tries to realize the EWS, so it is necessary to configure the addition of input data, where data from sensors and meteorological data is required to predict floods accurately. The purpose of this system is to make decisions and determine the flooding area. In order to achieve this goal, Decision Support System (DSS) techniques with primary and secondary data are applied. Primary and secondary data as input of Fuzzy Multiple Attribute Decision Making (FMADM) algorithm. The expectation is based on weight, normalized model to get optimal prediction result. The EWS equipped by sirens, short messages, websites, and also Android apps to provide monitoring and prediction information. The experiment was carried out using EWS hardware mounted on streams and the results indicated the good performance of the system with fulfill errors. © 2005 – ongoing JATIT & LLS.

Affiliations

Universitas Negeri Yogyakarta, Department of Electronics Education, Yogyakarta, Indonesia; Universitas Negeri Yogyakarta, Department of Informatics Education, Yogyakarta, Indonesia; Universitas Negeri Yogyakarta, Department of Geographic Education, Yogyakarta, Indonesia