Abstract
AbstractAccurate streamflow prediction is required in sustainable water resources management. Direct use of observed data in developing prediction models has resulted in inaccuracies. Discrete wavelet transform (DWT) is widely used to decompose observed data (raw data) into spectral bands and eliminate trends and periodicity to improve the accuracy of the models. However, DWT is known to have serious drawbacks, and predictions of daily streamflow have been with short lead times. In this study, a simple method called the SEASON algorithm was used to decompose the observed data into components with the objective of overcoming the drawbacks of DWT so that daily streamflow can be predicted with better accuracy and longer lead times. Data decomposed by SEASON and DWT were used as input into multilayer perceptron (MP) approaches to develop new approaches for predicting daily streamflow for lead times up to 7 days, and termed as seasonally adjusted series-multilayer perceptron (SAS-MP) and wavelet-multilayer per...
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