Application of novel binary optimized machine learning models for monthly streamflow prediction

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource, 1 online resource.

Language: Englisch

Bibliographic citation: Application of novel binary optimized machine learning models for monthly streamflow prediction ; volume:13 ; number:5 ; day:8 ; month:4 ; year:2023 ; pages:1-24 ; date:5.2023
Applied water science ; 13, Heft 5 (8.4.2023), 1-24, 5.2023

Creator: Adnan, Rana Muhammad
Dai, Hong-Liang
Mostafa, Reham R.
Towfiqul Islam, Abu Reza Md.
Kisi, Ozgur
Elbeltagi, Ahmed
Zounemat-Kermani, Mohammad

Contributor: SpringerLink (Online service)

DOI: 10.1007/s13201-023-01913-6

URN: urn:nbn:de:101:1-2023090610430646427910

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 11:02 AM CEST

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Adnan, Rana Muhammad
Dai, Hong-Liang
Mostafa, Reham R.
Towfiqul Islam, Abu Reza Md.
Kisi, Ozgur
Elbeltagi, Ahmed
Zounemat-Kermani, Mohammad
SpringerLink (Online service)

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Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

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Application of novel binary optimized machine learning models for monthly streamflow prediction

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Other Objects (12)

Monthly hydrometeorological ensemble prediction of streamflow droughts and corresponding drought indices

On complexity of optimized crossover for binary representations

Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts

Beyond binary baseflow separation: a delayed-flow index for multiple streamflow contributions

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

Optimized feature space learning and binary hashing for image retrieval

Beyond binary baseflow separation: delayed flow index as a fresh perspective on streamflow contributions

Correction to: Monthly Streamflow Modeling Based on Self-organizing Maps and Satellite-estimated Rainfall Data

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Streamflow classification by employing various machine learning models for peninsular Malaysia

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On complexity of optimized crossover for binary representations

Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts

Beyond binary baseflow separation: a delayed-flow index for multiple streamflow contributions

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

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On complexity of optimized crossover for binary representations

Evaluating residual error approaches for post-processing monthly and seasonal streamflow forecasts

Beyond binary baseflow separation: a delayed-flow index for multiple streamflow contributions

Monthly Streamflow Modeling Based on Self-Organizing Maps and Satellite-Estimated Rainfall Data

Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow

Optimized feature space learning and binary hashing for image retrieval

Beyond binary baseflow separation: delayed flow index as a fresh perspective on streamflow contributions

Correction to: Monthly Streamflow Modeling Based on Self-organizing Maps and Satellite-estimated Rainfall Data

Enhancing spatial streamflow prediction through machine learning algorithms and advanced strategies

Short-term streamflow modeling using data-intelligence evolutionary machine learning models

Streamflow classification by employing various machine learning models for peninsular Malaysia

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