Show simple item record

AuthorShaofu, Ma
AuthorAl-Juboori, Anas Mahmood
AuthorAlwan, Asmaa Hussein
AuthorAbdel-Salam, Abdel-Salam G.
Available date2023-11-29T10:06:02Z
Publication Date2021
Publication NameComplexity
ResourceScopus
ISSN10762787
URIhttp://dx.doi.org/10.1155/2021/3721661
URIhttp://hdl.handle.net/10576/49814
AbstractStreamflow is associated with several sources on nonstationaries and hence developing machine learning (ML) models is always the motive to provide a reliable methodology to understand the actual mechanism of streamflow. The current research was devoted to generating monthly streamflows from annual streamflow. In this study, three different ML models were applied for this purpose, including Multiple Additive Regression Trees (MART), Group Methods of Data Handling (GMDH), and Gene Expression Programming (GEP). The models were developed based on annual streamflow and monthly time index of three rivers (i.e., Upper Zab, Lower Zab, and Diyala) located in the north region of Iraq. The modeling results indicated an optimistic simulation for generating the monthly streamflow time series from annual streamflow time series. The potential of the MART model was superior to the GMDH and GEP models for Upper Zab River (R2 0.84, 0.64, and 0.47), Lower Zab River (R2 0.75, 0.46, and 0.40), and Diyala River (R2 0.78, 0.42, and 0.5). The results of RMSE were 113, 169, and 208 for Upper Zab River, 95, 149, and 0.5 for Lower Zab River, and 73, 118, and 109 for Diyala River. The results have proved the possibility of changing the timescale in generating streamflow data.
Languageen
PublisherHindawi Limited
SubjectData handling
Gene expression
Machine learning
Rivers
Time series
Trees (mathematics)
Additive regression
Gene expression programming
Machine learning models
Model results
Optimistic simulation
River flow
Time index
Time-scales
Stream flow
TitleOn the Investigation of Monthly River Flow Generation Complexity Using the Applicability of Machine Learning Models
TypeArticle
Volume Number2021
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record