Xuan-Hien Le

Associate Professor

Chuo University

Japan

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Xuan-Hien Le is an Associate Professor at Chuo University in Japan. His research focuses on the integration of numerical and machine learning methods for hydraulic modeling, specifically in relation to discharge coefficients, rainfall integration, and scour depth estimation. Recent articles highlight his work on climate change impacts, streamflow forecasting, and soil organic carbon stock predictions. He is actively contributing to advancements in the field through innovative methodologies and comparative studies.

Articles (19)

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2025

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2024

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2023

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2023

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2023

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2023

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2023

Collaborators (1)

Duc Hai Nguyen

University of Saskatchewan

CANADA

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Xuan-Hien Le

Research Interests

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Articles (19)

Integrating numerical and regression methods for estimating discharge coefficients of intake structures with wingwalls in irrigation networks

Integrating numerical and regression methods for estimating discharge coefficients of intake structures with wingwalls in irrigation networks

Convolutional neural networks-driven bias correction of satellite precipitation improves rainfall-runoff-inundation modeling

Evaluating Geostatistical and Statistical Merging Methods for Radar–Gauge Rainfall Integration: A Multi-Method Comparative Study

Evaluating Geostatistical and Statistical Merging Methods for Radar-Gauge Rainfall Integration: A Multi-Method Comparative Study

Integrating machine learning and empirical approaches for scour depth estimation at sluice gates: evaluating tree-based models, hyperparameter tuning, and proposing new formulas

Quantifying the Impacts of Climate Change and Human Interventions on Flow Alterations in a Tropical River

Performance and uncertainty analysis in deep learning frameworks for streamflow forecasting via Monte Carlo dropout technique

Quantifying Predictive Uncertainty and Feature Selection in River Bed Load Estimation: A Multi-Model Machine Learning Approach with Particle Swarm Optimization

Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets

National variability in soil organic carbon stock predictions: Impact of bulk density pedotransfer functions

Improving rainfall-runoff modeling in the Mekong river basin using bias-corrected satellite precipitation products by convolutional neural networks

Predicting maximum scour depth at sluice outlet: a comparative study of machine learning models and empirical equations

Advanced hybrid techniques for predicting discharge coefficients in ogee-crested spillways: integrating physical, numerical, and machine learning models

Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea

Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea

Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study

Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study

Machine learning approaches for reconstructing gridded precipitation based on multiple source products

Collaborators (1)

Duc Hai Nguyen

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