EXPONENTIAL SMOOTHING TECHNIQUES ON TIME SERIES RIVER WATER LEVEL DATA

The increasing of river water level usually happens during raining season. This event can lead to devastating flash flood, which would eventually cause damage to properties and possibly, loss of human life. Such event is also known as extreme event due to the nature of the data produced, which mostly consist of nonlinear pattern of data. The existence of nonlinear pattern and noise data greatly affect the quality of prediction result. 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 Perlis, Malaysia. From the experiments, it was found that all the three techniques have their own limitations in handling extreme data, with Double Exponential Smoothing Technique to perform better than its counterpart.