Abstract
Flood is commonly known as one of the most frequent type of natural disaster worldwide that occurred when the ground is not able to absorb and accommodate the heavy rainfall. Flood might be caused by increased levels of river water more than the river bank or the dams. Many methods have been proposed to forecast the rainfall distribution but mostly the forecasting accuracy of the existing method are questionable. In this paper, a fuzzy time series method was proposed to forecast the rainfall distribution. The objectives of this paper, first is to formulate fuzzy spatial forecasting model for rainfall distribution for each month in Perlis. Second, to predict the accurate rainfall values in future for early warning of flood in order to reduce flood issues. Using the fuzzy spatial forecasting method, the historical data of rainfall in Perlis were used to forecast. After that, several rules was applied to determine whether the rainfall forecasting trend value goes downward or upward movement Then, the mean square error (MSE) was calculated to compare the forecasting rainfall results of various forecasting method. The smaller the value of MSE, the better the forecasting model. The monthly historical rainfall distribution in Perlis for 4 years had been used to illustrate the forecasting algorithm of the new fuzzy time series method. The experimental results of this research exhibited higher forecasting accuracy for forecasting rainfall compare to existing methods.
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Acknowledgments
The study was funded by “Long Term Research Grant (LRGS) (UUM/RIMPC/P-30)” and the authors also thank the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA for providing the laboratory facilities for completing the study.
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Othman, M., Azahari, S.N.F., Abu Massuut, N.A. (2016). Fuzzy Spatial Forecasting Model of Rainfall Distribution for Flood Early Warning. In: Yacob, N., Mohamed, M., Megat Hanafiah, M. (eds) Regional Conference on Science, Technology and Social Sciences (RCSTSS 2014). Springer, Singapore. https://doi.org/10.1007/978-981-10-0534-3_24
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DOI: https://doi.org/10.1007/978-981-10-0534-3_24
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