River water level time-series forecasting by using smoothing technique

Abstract

The increasing of river water level usually happens during raining season and can lead to devastating flash floods. Therefore, forecasting river water level series using the exponential smoothing method was applied to predict accurate river water level series. Three exponential smoothing techniques have been investigated to study their ability in handling extreme river water level time series data, which are Single Exponential Smoothing Technique, Double exponential smoothing technique and Holt’s Method. The techniques were performed on river water level data from three rivers in Pahang, Malaysia which is Sungai Jelai in Jeram Bungor which is case study 1, Sungai Tembeling which is case study 2 and Sungai Temerloh which is case study 3. Monthly data of Sungai Pahang water level was obtained from JPS Malaysia from January 2010 to February 2021. The IBM SPSS software was used to analyse this data. This method of forecasting is evaluated to determine the ability in the forecasting river water level for short-term forecast with seasonal and non-seasonal data. Based on the error generated from the analysis, Simple exponential smoothing technique from case study 1 was found to be the best model smoothing technique as it produced the lowest MAPE error which is 0.09 % as it suitable for short-term forecasting in 6 months ahead. The selection of seasonal data in cases studies 2 and 3 while non-seasonal data in case study 1 also showed different situations in the forecasting results.  By finding the best smoothing technique for extreme data, more accurate predictions can be produced. An accurate prediction is likely to be able to help the authority and the public in reducing the impact of flood disasters, and to act as an early warning system to inform the public about upcoming events.