Long-term natural streamflow forecasting under drought scenarios using data-intelligence modeling
Long-term river streamflow prediction and modeling are essential for water resource management and decision-making related to water resources. This research paper considers the importance of these predictions and proposes a model to address scarcity scenarios to support decision-making in water allo...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-01-01
|
| Series: | Water Cycle |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666445324000242 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Long-term river streamflow prediction and modeling are essential for water resource management and decision-making related to water resources. This research paper considers the importance of these predictions and proposes a model to address scarcity scenarios to support decision-making in water allocation, flood management, and drought prediction scenarios. Machine learning (ML) techniques offer promising alternatives for improving long-term streamflow prediction. However, most existing studies on ML models for streamflow prediction have focused on shorter time horizons, limiting their broader applicability. Consequently, there is a need for dedicated research that addresses the use of ML models in long-term streamflow prediction. Considering this research gap, this paper presents an ML-based approach that learns and replicates the natural flow dynamics of a river, allowing for the simulation of reduced flow scenarios (25 % and 50 % reduction). This capability allows for simulating drought scenarios of varying severity, providing valuable insights for water service managers. This study significantly contributes to the progress of predicting long-term river streamflow through the application of machine learning models. Moreover, this study offers valuable insights and recommendations for hydrologists to improve future research efforts. |
|---|---|
| ISSN: | 2666-4453 |