Predictive analytics modifications in wavelet: case study on Songkhla Lake basin runoff

Abstract

Accurate short-term rainfall-runoff prediction is very important for flood mitigation and the safety of infrastructures in Southern Thailand. This study aims to utilize both analysis and prediction of the runoff forecast by combining the wavelet technique with regression and artificial neural network.

The daily rainfall and runoff data were collected from 1,031 days during January 2017 to October 2019 in Songkhla Lake Basin, Thailand. The performance of calibration and validation of the models is evaluated with appropriate statistical methods; coefficient of determination (R²), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (ENS). The results of daily runoff series modeling indicated that the wavelet artificial neural network model performed the best among those models. This model showed the Coefficient of Determination, Nash-Sutcliffe Efficiency and Root mean Square Error in the value of 0.9999, 0.9998 and 0.0037, respectively. These values explained that the model can describe 99.99% of the variation of the current runoff in Songkhla Lake Basin.