Flood Susceptibility Prediction via Data-Mining Based Bell-Curve Analogical-Hydrographs Analysis: A Case Study of Langat River Basin, Selangor, Malaysia

In this study, we proposed data-mining based bell-curve analogical hydrographs analysis with lag time vertical axes and bankfull discharge horizontal axes to make flood susceptibility prediction. We utilized flood data reports, hourly/daily rainfall data and daily water discharge of Hulu Langat district, Selangor Malaysia from the year 2013-2016 to do flood susceptibility. We implement data mining concept by sorting the database, followed by plotting hydrograph to identify flood patterns and establish relationships to predict flood trends. This method is an intersection between the knowledge field of hydrology and mathematical modeling. When an outlier from the graph is detected, the knowledge from hydrology can be applied to understand the reason behind the appearance of outliers. Besides, the knowledge of mathematical modeling is necessary to assist us in predicting flood susceptibility. The purpose of this study is to predict the flood susceptibility which is vital to prepare the users/public well prepared for smooth and efficient evacuation. In 4 years context, our flood depth predictions are nearly 100% accurate. Factor influencing the lag time and steepness of rising limb are related to land use and topographical features. Implications of the results and future research directions are also presented.