Application of deep learning method for daily streamflow time-series prediction: A case study of the kowmung river at Cedar Ford, Australia
error analysis; global climate; machine learning; precision; prediction; streamflow; time series analysis; Australia
Saved in:
Main Authors: | Latif S.D., Ahmed A.N. |
---|---|
Other Authors: | 57216081524 |
Format: | Article |
Published: |
International Information and Engineering Technology Association
2023
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Time-series prediction of streamflows of Malaysian rivers using data-driven techniques
by: Pandhiani, Siraj Muhammed, et al.
Published: (2020) -
Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
by: Latif S.D., et al.
Published: (2024) -
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series
by: Mohammadi, Babak, et al.
Published: (2020) -
Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series
by: Mohammadi B., et al.
Published: (2023) -
The cost of producing biofuel from eastern red cedar in Oklahoma
by: Ramli, Nurul Nadia, et al.
Published: (2018)