This repository includes all the scripts related to the paper titled "A Novel Stochastic Tree Model For Daily Streamflow Prediction Based on A Noise Suppression Hybridization Algorithm and Efficient Uncertainty Quantification" Authored by Nasrin Fathollahzadeh Attar, Mohammad Taghi Sattari, and Halit Apaydin
You can find all attached data of this study (weekly and fortnight streamflow data) at: link1, link2
Usage
1- This repository written in R software, first of all two softwares of R and Rstudio should be downloaded.
2-Quick Test file shows all the methods and their codes with a real example.
3- Tree models conducted in WEKA software.
The flowchart of the study is as below:
The results of the CEEMDAN IMFs for the weekly streamflow data in Gazvin:
The results of the CEEMDAN IMFs for the fortnight streamflow data in Gazvin:
CEEMDAN for logarithmic demand, note that increasing ensemble size will produce smoother results (WEEKly data):
CEEMDAN for logarithmic demand, note that increasing ensemble size will produce smoother results (Fortnight data):
