Exploring alternate coupling inputs of a data-driven model for optimum daily streamflow prediction in calibrated SWAT-BiLSTM rainfall-runoff modeling

Exploring alternate coupling inputs of a data-driven model for optimum daily streamflow prediction in calibrated SWAT-BiLSTM rainfall-runoff modeling

Accurate streamflow prediction in mountainous regions is vital for sustaining water resources in downstream areas, ensuring reliable availability for agriculture, energy, and consumption. However, physically based prediction models are prone to substantial uncertainties due to complex processes and...

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Bibliographic Details
Main Authors: Khalil Ahmad, Mudassar Iqbal, Muhammad Atiq Ur Rehman Tariq, Afed Ullah Khan, Abdullah Nadeem, Jinlei Chen, Kseniia Usanova, Hamad Almujibah, Hashem Alyami, Muhammad Abid
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Water
Subjects:
advancement in rainfall-runoff
BiLSTM
inputs selection
streamflow prediction
SWAT-BiLSTM modeling
supervised machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/frwa.2025.1558218/full
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https://www.frontiersin.org/articles/10.3389/frwa.2025.1558218/full

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