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Identifying Opportunities to Improve Daily Streamflow Predictions Using Machine Learning

https://doi.org/10.2139/ssrn.4855490
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inferredcites →https://doi.org/10.1002/2014wr015348openalex-extracted
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inferredcites →https://doi.org/10.1002/wics.14openalex-extracted
Ridge regression
inferredcites →https://doi.org/10.1007/s00477-013-0755-5openalex-extracted
Observed hydrologic non-stationarity in far south-eastern Australia: implications for modelling and prediction
inferredcites →https://doi.org/10.1007/s10462-020-09896-5openalex-extracted
A comparative analysis of gradient boosting algorithms
inferredcites →https://doi.org/10.1007/s10661-016-5094-9openalex-extracted
An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland
inferredcites →https://doi.org/10.1007/s10994-019-05787-1openalex-extracted
Accelerated gradient boosting
inferredcites →https://doi.org/10.1007/s11269-020-02483-xopenalex-extracted
A Hybrid Model Based on Variational Mode Decomposition and Gradient Boosting Regression Tree for Monthly Runoff Forecasting
inferredcites →https://doi.org/10.1007/s11269-020-02619-zopenalex-extracted
Developing Novel Robust Models to Improve the Accuracy of Daily Streamflow Modeling
inferredcites →https://doi.org/10.1007/s11269-020-02659-5openalex-extracted
Employing Machine Learning Algorithms for Streamflow Prediction: A Case Study of Four River Basins with Different Climatic Zones in the United States
inferredcites →https://doi.org/10.1007/s11269-021-02937-wopenalex-extracted
Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
inferredcites →https://doi.org/10.1007/s12665-015-5096-xopenalex-extracted
A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction
inferredcites →https://doi.org/10.1016/0022-1694(94)90057-4openalex-extracted
Optimal use of the SCE-UA global optimization method for calibrating watershed models
inferredcites →https://doi.org/10.1016/bs.host.2020.01.002openalex-extracted
Machine learning algorithms, applications, and practices in data science
inferredcites →https://doi.org/10.1016/j.ecolecon.2010.11.010openalex-extracted
Significance of environment in the assessment of sustainable development: The case for south west Victoria
inferredcites →https://doi.org/10.1016/j.envsoft.2017.12.021openalex-extracted
Improving predictions of hydrological low-flow indices in ungaged basins using machine learning
inferredcites →https://doi.org/10.1016/j.eswa.2017.04.003openalex-extracted
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inferredcites →https://doi.org/10.1016/j.eswa.2019.03.011openalex-extracted
Constraint learning based gradient boosting trees
inferredcites →https://doi.org/10.1016/j.jhydrol.2006.12.015openalex-extracted
Data based mechanistic model for low flows: Implications for the effects of climate change
inferredcites →https://doi.org/10.1016/j.jhydrol.2015.10.038openalex-extracted
Artificial intelligence based models for stream-flow forecasting: 2000–2015
inferredcites →https://doi.org/10.1016/j.jhydrol.2016.03.017openalex-extracted
Forecasting daily streamflow using online sequential extreme learning machines
inferredcites →https://doi.org/10.1016/j.jhydrol.2016.09.035openalex-extracted
Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq
inferredcites →https://doi.org/10.1016/j.jhydrol.2017.10.031openalex-extracted
Modeling flash floods in ungauged mountain catchments of China: A decision tree learning approach for parameter regionalization
inferredcites →https://doi.org/10.1016/j.jhydrol.2018.07.004openalex-extracted
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
inferredcites →https://doi.org/10.1016/j.jhydrol.2019.124219openalex-extracted
Investigating strategies to improve hydrologic model performance in a changing climate
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.124658openalex-extracted
Enhanced low flow prediction for water and environmental management
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.125168openalex-extracted
Estimating annual runoff in response to forest change: A statistical method based on random forest
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.125376openalex-extracted
Long lead-time daily and monthly streamflow forecasting using machine learning methods
inferredcites →https://doi.org/10.1016/j.jhydrol.2021.126505openalex-extracted
Towards more realistic runoff projections by removing limits on simulated soil moisture deficit
inferredcites →https://doi.org/10.1016/j.jhydrol.2022.127429openalex-extracted
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inferredcites →https://doi.org/10.1016/j.jhydrol.2023.129480openalex-extracted
Monthly streamflow forecasting by machine learning methods using dynamic weather prediction model outputs over Iran
inferredcites →https://doi.org/10.1016/j.jhydrol.2023.130141openalex-extracted
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inferredcites →https://doi.org/10.1016/j.jhydrol.2023.130185openalex-extracted
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inferredcites →https://doi.org/10.1016/j.scitotenv.2019.134308openalex-extracted
Evaluation of Soil and Water Assessment Tool and Artificial Neural Network models for hydrologic simulation in different climatic regions of Asia
inferredcites →https://doi.org/10.1016/s0022-1694(03)00225-7openalex-extracted
Improvement of a parsimonious model for streamflow simulation
inferredcites →https://doi.org/10.1029/2019wr026226openalex-extracted
Data Assimilation for Streamflow Forecasting Using Extreme Learning Machines and Multilayer Perceptrons
inferredcites →https://doi.org/10.1029/2022wr033692openalex-extracted
The Role of Weather System Changes and Catchment Characteristics in the Rainfall‐Runoff Relationship Shift in Victoria, Australia
inferredcites →https://doi.org/10.1071/es09032openalex-extracted
High-quality spatial climate data-sets for Australia
inferredcites →https://doi.org/10.1080/02626667.2019.1639716openalex-extracted
Streamflow prediction in ungauged basins: analysis of regionalization methods in a hydrologically heterogeneous region of Mexico
inferredcites →https://doi.org/10.1080/10485252.2012.715161openalex-extracted
Robustness of random forests for regression
inferredcites →https://doi.org/10.1080/19942060.2022.2119281openalex-extracted
Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow
inferredcites →https://doi.org/10.1088/1748-9326/aba927openalex-extracted
Machine learning assisted hybrid models can improve streamflow simulation in diverse catchments across the conterminous US
inferredcites →https://doi.org/10.1109/access.2015.2450498openalex-extracted
High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications
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Streamflow Hydrology Estimate Using Machine Learning ( SHEM )
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Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria
inferredcites →https://doi.org/10.2166/wst.2013.491openalex-extracted
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inferredcites →https://doi.org/10.3390/hydrology8010006openalex-extracted
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inferredcites →https://doi.org/10.3390/hydrology9010005openalex-extracted
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inferredcites →https://doi.org/10.3390/w15142572openalex-extracted
Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models
inferredcites →https://doi.org/10.5194/hess-20-3947-2016openalex-extracted
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inferredcites →https://doi.org/10.5194/hess-25-2997-2021openalex-extracted
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inferredcites →https://doi.org/10.5194/hess-25-3897-2021openalex-extracted
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inferredcites →https://doi.org/10.5194/hess-25-5517-2021openalex-extracted
Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models
inferredcites →https://doi.org/10.5194/hess-26-6073-2022openalex-extracted
Explaining changes in rainfall–runoff relationships during and after Australia's Millennium Drought: a community perspective
inferredcites →https://doi.org/10.5822/978-1-59726-228-6_5openalex-extracted
Australia’s Millennium Drought: Impacts and Responses
inferredcites →https://doi.org/10.22499/2.5804.003openalex-snapshot
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