Study of Water Level-Discharge Relationship Using Artificial Neural Network (ANN) in Sungai Gumum, Tasik Chini Pahang Malaysia

摘要

The prediction of discharge (Q) and its variability in a river and lake are an essential component of hydrological regime studies. For the purpose, two tasks were developed to study the relationship in the Sungai Gumum and Tasik Chini Pahang. First, using simple functional relationship between water level and Q and expressed as a rating curve. Second, using complex non-linear Artificial Neural Network (ANN) method to train and validate the Q data of Sungai Gumum and its relationship to Tasik Chini water level fluctuation. The rating curve indicates that maximum Q was calculated at 0.09 m3 sec-1 at 0.64 m depth and the minimum of 0.02 m3 sec-1 at 0.1 m depth. Meanwhile, the ANN model explains 65.9% of the validation data set yielded result within 5% of error in predicting the stream Q. The relationship between ANN prediction of Q and the mean water level of Tasik Chini show highly positive correlation (R2 = 0.89). This indicates that Sungai Gumum plays a vital role in supplying fresh water into Tasik Chini. Restoration of the hydrological aspects through regulating the water level in Tasik Chini is essential to ensure prolongs water-based activities.