Abstract
A knowledge-based framework for mitigation of hydro-meteorological disasters in Brunei is proposed where a data mining process is used to predict anomalous intense rainfalls that were causing destructive floods and landslides. A previous study pointed the causes to anomalous oceanographic and atmospheric conditions. This expert knowledge and satellite data is used to create the model. Interoperable collaborative platforms are also crucial. This approach can potentially alter the prevailing disaster management where reactive response dominated the proactive bottom-up approach of disaster mitigation based on expert knowledge-based prediction from data mining.
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Acknowledgements
The authors gratefully acknowledge the kind assistance given by Mr. Anthony Banyouko Ndah, one of the authors of [3] for explanations and guidance, in giving permission for use of parts of their findings for the works above and the use of conceptual framework in Fig. 2. The authors also acknowledge and grateful for the valuable helpful comments and suggestions of the reviewers, which have improved the content and presentation of this paper.
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Badarudin, P.H.A.P., Au, T.W., Phon-Amnuaisuk, S. (2017). A Knowledge-Based Framework for Mitigating Hydro-Meteorological Disasters. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_50
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DOI: https://doi.org/10.1007/978-3-319-61845-6_50
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