An Intelligent Crisis-Mapping Framework for Flood Prediction
Siti Azirah Asmai1, Zaheera Zainal Abidin2, Halizah Basiron3, Sabrina Ahmad4
1Siti Azirah Asmai, Faculty of Information and Communication Technology, Universiti Teknikal, Melaka, Malaysia.
2Zaheera Zainal Abidin, Faculty of Information and Communication Technology, Universiti Teknikal, Melaka, Malaysia.
3Halizah Basiron, Faculty of Information and Communication Technology, Universiti Teknikal, Melaka, Malaysia.
4Sabrina Ahmad, Faculty of Information and Communication Technology, Universiti Teknikal, Melaka, Malaysia.
Manuscript received on 20 August 2019 | Revised Manuscript received on 11 September 2019 | Manuscript Published on 17 September 2019 | PP: 1304-1310 | Volume-8 Issue-2S8 August 2019 | Retrieval Number: B10580882S819/2019©BEIESP | DOI: 10.35940/ijrte.B1058.0882S819
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper proposes a new framework for crisis-mapping with flood prediction model based on the crowdsourcing data. Crisis-mapping is still at infancy stage development and offers opportunities for exploration. In fact, the application of the crisis-mapping gives fast information delivery and continuous updates for crisis and emergency evacuation using sensors. However, current crisis-mapping is more to the information dissemination of flood-related information and lack of flood prediction capability. Therefore, this paper applied artificial neural network for flood prediction model in the proposed framework. Sensor data from the crowdsourcing platform can be used to predict the flood-related measures to support continuous flood monitoring. In addition, the proposed framework makes used of the unstructured data from the Twitters to support the flood warnings dissemination to locate flood area with no sensor installation. Based on the results of the experiment, the fitted model from the optimization process gives 90.9% of accuracy performance. The significance of this study is that we provide a new alternative in flood warnings dissemination that can be used to predict and visualized the flood occurrence. This prediction is significant to agencies and authorities to identify the flood risk before its occurrence and crisis-maps can be used as an analytics tool for future city planning.
Keywords: Crisis-Mapping, Flood Prediction, Crowdsourcing, Artificial Neural Network, Visualization.
Scope of the Article: Regression and Prediction