Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins

No abstract is available for this paper.

• Publication year

  2024

• Venue

  Journal of Hydrology

• Publication date

  2024-08-01

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1016/j.jhydrol.2024.131598
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

  2024cited by this paper
• Global prediction of extreme floods in ungauged watersheds

  2024cited by this paper
• Deep learning for cross-region streamflow and flood forecasting at a global scale

  2024cited by this paper
• Unravelling and improving the potential of global discharge reanalysis dataset in streamflow estimation in ungauged basins

  2023cited by this paper
• Deep Transfer Learning based on Transformer for Flood Forecasting in Data-sparse Basins

  2023cited by this paper
• High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections

  2023cited by this paper
• Bias Correcting Discharge Simulations from the GEOGloWS Global Hydrologic Model

  2023cited by this paper
• Dimensional analysis of literature formulas to estimate the characteristic flood response time in ungauged basins: a velocity-based approach

  2023cited by this paper
• Reliability-weighted approach for streamflow prediction at ungauged catchments

  2023cited by this paper
• Exploring Signature‐Based Model Calibration for Streamflow Prediction in Ungauged Basins

  2023cited by this paper
• Caravan - A global community dataset for large-sample hydrology

  2023cited by this paper
• Automatic Regionalization of Model Parameters for Hydrological Models

  2022cited by this paper
• Global-Scale Characterization of Streamflow Extremes

  2022cited by this paper
• Interpretable tree-based ensemble model for predicting beach water quality.

  2022cited by this paper
• Estimating the Routing Parameter of the Xin'anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

  2022cited by this paper
• Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model

  2022cited by this paper
• Regionalization of hydrological model parameters using gradient boosting machine

  2021cited by this paper
• Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization

  2021cited by this paper
• Shedding light on “Black Box” machine learning models for predicting the reactivity of HO radicals toward organic compounds

  2021cited by this paper
• Supplementary material to "ERA5-Land: A state-of-the-art global reanalysis dataset for land applications"

  2021cited by this paper
• Flood exposure and poverty in 188 countries

  2021cited by this paper
• Hydrological Concept Formation inside Long Short-Term Memory (LSTM) networks

  2021cited by this paper
• A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness

  2021cited by this paper
• Regionalization for Ungauged Catchments — Lessons Learned From a Comparative Large‐Sample Study

  2021cited by this paper
• Using machine learning methods for supporting GR2M model in runoff estimation in an ungauged basin

  2021cited by this paper
• Regionalization of hydrological models for flow estimation in ungauged catchments in Ireland

  2021cited by this paper
• CAMELS-AUS: Hydrometeorological time series and landscape attributes for 222 catchments in Australia

  2021cited by this paper
• Prediction and analysis of train arrival delay based on XGBoost and Bayesian optimization

  2021cited by this paper
• LamaH | Large-Sample Data for Hydrology: Big data für die Hydrologie und Umweltwissenschaften

  2021cited by this paper
• A history of TOPMODEL

  2020cited by this paper
• From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling

  2020cited by this paper
• Predicting stream water quality under different urban development pattern scenarios with an interpretable machine learning approach.

  2020cited by this paper
• Hydrologically Informed Machine Learning for Rainfall-Runoff Modelling: Towards Distributed Modelling

  2020cited by this paper
• Evaluating the performance of random forest for large-scale flood discharge simulation

  2020cited by this paper
• A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds

  2020cited by this paper
• CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil

  2020cited by this paper
• CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

  2020cited by this paper
• A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning

  2020cited by this paper
• Effects of microplastics on growth, phenanthrene stress, and lipid accumulation in a diatom, Phaeodactylum tricornutum.

  2019cited by this paper
• Prediction and Inference of Flow Duration Curves Using Multioutput Neural Networks

  2019cited by this paper
• Explaining individual predictions when features are dependent: More accurate approximations to Shapley values

  2019cited by this paper
• Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

  2019cited by this paper
• Deep learning and process understanding for data-driven Earth system science

  2019cited by this paper
• Explainable extreme gradient boosting tree-based prediction of toluene, ethylbenzene and xylene wet deposition.

  2019cited by this paper
• Deep learning based multi-temporal crop classification

  2019cited by this paper
• Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution

  2019cited by this paper
• Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

  2019cited by this paper
• Diagnostic Evaluation of Large‐Domain Hydrologic Models Calibrated Across the Contiguous United States

  2019cited by this paper
• Deep Learning with a Long Short-Term Memory Networks Approach for Rainfall-Runoff Simulation

  2018cited by this paper
• How to explain and predict the shape parameter of the generalized extreme value distribution of streamflow extremes using a big dataset

  2018cited by this paper
• A Ranking of Hydrological Signatures Based on Their Predictability in Space

  2018cited by this paper
• The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

  2018cited by this paper
• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

  2018cited by this paper
• In Quest of Calibration Density and Consistency in Hydrologic Modeling: Distributed Parameter Calibration against Streamflow Characteristics

  2018cited by this paper
• Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

  2018cited by this paper
• A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring

  2017cited by this paper
• Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization

  2017cited by this paper
• Towards seamless large‐domain parameter estimation for hydrologic models

  2017cited by this paper
• Interpretable Explanations of Black Boxes by Meaningful Perturbation

  2017cited by this paper
• Streamflow estimation in ungauged catchments using regionalization techniques

  2017cited by this paper
• A Unified Approach to Interpreting Model Predictions

  2017cited by this paper
• The CAMELS data set: catchment attributes and meteorology for large-sample studies

  2017cited by this paper
• XGBoost: A Scalable Tree Boosting System

  2016cited by this paper
• Improving the theoretical underpinnings of process‐based hydrologic models

  2016cited by this paper
• On evaluating the robustness of spatial-proximity-based regionalization methods

  2016cited by this paper
• How should a rainfall‐runoff model be parameterized in an almost ungauged catchment? A methodology tested on 609 catchments

  2016cited by this paper
• Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines

  2015cited by this paper
• Use of a parsimonious rainfall–run‐off model for predicting hydrological response in ungauged basins

  2015cited by this paper
• Explaining prediction models and individual predictions with feature contributions

  2014cited by this paper
• Comparison of regionalization approaches in parameterizing sediment rating curve in ungauged catchments for subsequent instantaneous sediment yield prediction

  2014cited by this paper
• Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

  2013cited by this paper
• Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations

  2013cited by this paper
• A decade of Predictions in Ungauged Basins (PUB)—a review

  2013cited by this paper
• Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

  2013cited by this paper
• From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models

  2012cited by this paper
• A review of regionalisation for continuous streamflow simulation

  2011cited by this paper
• Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale

  2010cited by this paper
• Gauging the ungauged basin: how many discharge measurements are needed?

  2009cited by this paper
• Regionalization as a learning process

  2009cited by this paper
• Spatial proximity, physical similarity, regression and ungaged catchments: A comparison of regionalization approaches based on 913 French catchments

  2008cited by this paper
• Comparison of four regionalisation methods for a distributed hydrological model

  2007cited by this paper
• Global water-balance modelling with WASMOD-M: Parameter estimation and regionalisation

  2007cited by this paper
• Split-parameter structure for the automatic calibration of distributed hydrological models

  2007cited by this paper
• Calibration of hydrological model parameters for ungauged catchments

  2006cited by this paper
• Ensemble predictions of runoff in ungauged catchments

  2005cited by this paper
• A comparison of regionalisation methods for catchment model parameters

  2005cited by this paper
• Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system

  2004cited by this paper
• Regionalisation of catchment model parameters

  2004cited by this paper
• Predicting daily flows in ungauged catchments: model regionalization from catchment descriptors at the Coweeta Hydrologic Laboratory, North Carolina

  2003cited by this paper
• A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters

  2002cited by this paper
• Probabilistic optimization for conceptual rainfall‐runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms

  1999cited by this paper
• Regionalisation of parameters for a conceptual rainfall-runoff model

  1999cited by this paper
• Multi-objective global optimization for hydrologic models

  1998cited by this paper
• Scale issues in hydrological modelling: A review

  1995cited by this paper
• Effective and efficient global optimization for conceptual rainfall‐runoff models

  1992cited by this paper
• THE PREDICTION OF HILLSLOPE FLOW PATHS FOR DISTRIBUTED HYDROLOGICAL MODELLING USING DIGITAL TERRAIN MODELS

  1991cited by this paper
• A physically based, variable contributing area model of basin hydrology

  1979cited by this paper

• Interpretable deep learning for sewer network water level forecasting in a Northern Chinese City: Implications for enhancing real-time assessment of system operational conditions

  2026cites this paper
• A Novel Hybrid Predictive Model Based on Mixture Density Networks With Weighted Conformal Inference Strategy for Runoff Interval Prediction Across Australia

  2026cites this paper
• Bridging prediction and optimization: A unified MINLP, traditional machine learning and deep learning framework for water distribution networks management

  2026cites this paper
• Integrated Random Forest and APEX-MODFLOW model for predicting and mapping river salinity in the Animas Watershed, Colorado River Basin

  2026cites this paper
• Modeling runoff with incomplete data: a comparison of hydrological, deep learning, and hybrid approaches

  2026cites this paper
• Innovative daily runoff prediction model integrating black-winged kite algorithm and Mamba2-Transformer architecture

  2026cites this paper
• Performance enhancement of deep learning model with attention mechanism and FCN model in flood forecasting

  2025cites this paper
• Machine-Learning-Based Ensemble Prediction of the Snow Water Equivalent in the Upper Yalong River Basin

  2025cites this paper
• Early warning forecasting of river ecological flow in a typical Karst basin, southwest China: Leveraging baseflow segmentation and interpretable machine learning

  2025cites this paper
• Forecasting monthly runoff in a glacierized catchment: A comparison of extreme gradient boosting (XGBoost) and deep learning models

  2025cites this paper
• Pooling local climate and donor gauges with deep learning for improved reconstructions of streamflow in ungauged and partially gauged basins

  2025cites this paper
• A novel TCN-augmented CNN-LSTM architecture for accurate monthly runoff forecasting

  2025cites this paper
• Exploring the influence of hydrological indicators on flow regimes through a data-driven modeling approach in the Huai River Basin, China.

  2025cites this paper
• A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning

  2025cites this paper
• Quantifying the Drivers and Constraints of Runoff and Sediment Yields: Insights From the Xiangjiang River Basin

  2025cites this paper
• A Review of XAI Methods Applications in Forecasting Runoff and Water Level Hydrological Tasks

  2025cites this paper
• Enhancing Streamflow Modeling in Data-Scarce Catchments with Similarity-Guided Source Selection and Transfer Learning

  2025cites this paper
• Cross-scale soil moisture content monitoring of winter wheat by integrating UAV and sentinel-1/2 data

  2025cites this paper
• Distributed Hybrid Flood Modeling Framework: Integrating Physical Mechanisms With Deep Learning for Enhanced Efficiency and Accuracy

  2025cites this paper
• Enhancing Hydrological Extremes Forecasting Capabilities in Data‐Scarce Regions Through Transfer Learning With Data Augmentation

  2025cites this paper
• An Explainable Flash Flood Prediction Model in the Qinling Mountains

  2025cites this paper
• Enhancing reference crop evapotranspiration prediction in arid regions: a stacking ensemble learning approach for the Amu Darya basin

  2025cites this paper
• Exploring Hydrological Variable Interconnections and Enhancing Predictions for Data‐Limited Basins Through Multi‐Task Learning

  2025cites this paper
• Daily runoff prediction for ungauged basins: Optimization of model transfer and selection of donor basins

  2025cites this paper
• Deriving hydrological inferences from a machine learning model to understand the physical drivers of flow duration curves

  2025cites this paper
• Surface water runoff estimation: a review of methods incorporating terrain shape

  2025cites this paper
• Assessing the role of precipitation inputs and overbank flow in hydrological modeling: a case study of the Irrawaddy River Basin in Myanmar using WRF-Hydro

  2025cites this paper
• Mixture of experts leveraging informer and LSTM variants for enhanced daily streamflow forecasting

  2025cites this paper
• The decontaminant mechanism of polyamide membranes for sulfamethoxazole: The insights from combined machine learning and molecular modelling

  2025cites this paper
• Machine learning-based prediction for airflow velocity in unpressured water-conveyance tunnels

  2025cites this paper
• Spatial heterogeneity of groundwater depths in coastal cities and their responses to multiple factors interactions by interpretable machine learning models

  2025cites this paper
• Blending daily satellite precipitation product and rain gauges using stacking ensemble machine learning with the consideration of spatial heterogeneity

  2025cites this paper
• Stream salinity prediction in data-scarce regions: Application of transfer learning and uncertainty quantification.

  2024cites this paper
• Regression-based machine learning approaches for estimating discharge from water levels in microtidal rivers

  2024cites this paper
• New perspectives on urban stormwater management in China, with a focus on extreme rainfall events

  2024cites this paper
• Driving Forces behind the Reduction in Cropland Area on Hainan Island, China: Implications for Sustainable Agricultural Development

  2024cites this paper