Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia

Machine learning-based hydrograph modeling with LSTM: A case study in the Jatigede Reservoir Catchment, Indonesia

Predicting and comprehending river discharge is essential to Earth system science in order to manage water resources and preserve ecological sustainability. In data-scarce tropical regions, traditional hydrological models often face challenges due to limited observations and high variability in rain...

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Bibliographic Details
Main Authors: Neil Andika, Piter Wongso, Faizal Immaddudin Wira Rohmat, Siska Wulandari, Ammar Fadhil, Riswanto Rosi, Nabila Siti Burnama
Format: Article
Language:English
Published: Elsevier 2025-12-01
Series:Results in Earth Sciences
Subjects:
Machine learning
Hydrograph modeling
LSTM
Jatigede Reservoir
Online Access:http://www.sciencedirect.com/science/article/pii/S2211714825000329
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