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Monday, July 7, 2014

SEDS Elements in "Flood Risk Pattern Recognition" in Malaysia

My SETDS methodology should work fine with any datetime stamped data. So here is an attempt to apply some similar technique for "Flood Risk Pattern Recognition" in Malaysia:

[PDF] Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin  (Computational Water, Energy, and Environmental Engineering) by Ahmad Shakir Mohd Saudi and others

..."Time Series Analysis is essential for the prediction of water level in the study area, where this method enables
an efficient evaluation of the process from the performance by analyzing data. The method produces three important
data (e.g., Upper Control Limit (UCL), Average Value (AVG) and Lower Control Limit (LCL)) for the
trend and prediction of future hydrological modelling, where the Sigma is within a range value of a set of data.Control Chart can detect some trends and patterns with actual data deviations from historical baseline, be able to capture unusual resource usage, can determine the dynamic threshold, and also can become the best base lining to examine the actual data deviation from the historical baseline (Igor Trubin, 2008) [7]. The equation implementedin this analysis was:
                                     Moving Range = Plot : MRt for t = 2, 3,, m.  ...
[7] Trubin, I.A. (2008) Exception Based Modelling and Forecasting. Proceedings of the Computer Measurement Group, Nevada, 7-12 December 2008, 353-364..."