Articles | Volume 27, issue 5
https://doi.org/10.5194/hess-27-1047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-27-1047-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
Symbiosis Institute of Geo-informatics, Symbiosis International (Deemed University), Pune 411016, India
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
Manu Vardhan
Computer Science and Engineering Department, National Institute of
Technology Raipur, Raipur 492010, India
Rakesh Sahu
Computer Science and Engineering Department, Chandigarh University, Mohali 140413, India
Debrupa Chatterjee
Symbiosis Institute of Geo-informatics, Symbiosis International (Deemed University), Pune 411016, India
Pankaj Chauhan
Geomorphology and Glaciology Department, Wadia Institute of Himalayan Geology, Dehradun 248001, India
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, China
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Cited
22 citations as recorded by crossref.
- Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework R. Vogeti et al. 10.2166/wcc.2024.594
- Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data R. Adnan et al. 10.1007/s00704-023-04624-9
- Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models C. Deng et al. 10.1016/j.ejrh.2024.101716
- Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches P. Chauhan et al. 10.1016/j.aiig.2024.100069
- Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
- Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
- Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
- Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods J. Wu et al. 10.1016/j.envres.2023.117272
- Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin Q. Nguyen et al. 10.2166/wcc.2023.313
- Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models B. Ullah et al. 10.1007/s11269-023-03645-3
- Divergent path: isolating land use and climate change impact on river runoff S. Mahmood et al. 10.3389/fenvs.2024.1338512
- Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios B. Mishra et al. 10.2166/wst.2024.011
- Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
- Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scales H. Han et al. 10.1016/j.ecolind.2023.110809
- Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective M. Khan et al. 10.2166/wst.2023.340
- GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
- Runoff Forecasting of Machine Learning Model Based on Selective Ensemble S. Liu et al. 10.1007/s11269-023-03566-1
- Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data O. Kisi et al. 10.1007/s00477-024-02692-5
- Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
- Performance evaluation of different gridded precipitation and CMIP6 model products with gauge observations for assessing rainfall variability under the historical and future climate change scenario over a semi-arid catchment, India D. Chatterjee et al. 10.1016/j.pce.2023.103433
- Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects B. Li et al. 10.1016/j.ejrh.2023.101547
- A novel framework for peak flow estimation in the himalayan river basin by integrating SWAT model with machine learning based approach S. Raaj et al. 10.1007/s12145-023-01163-9
22 citations as recorded by crossref.
- Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework R. Vogeti et al. 10.2166/wcc.2024.594
- Improved prediction of monthly streamflow in a mountainous region by Metaheuristic-Enhanced deep learning and machine learning models using hydroclimatic data R. Adnan et al. 10.1007/s00704-023-04624-9
- Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models C. Deng et al. 10.1016/j.ejrh.2024.101716
- Forecast future disasters using hydro-meteorological datasets in the Yamuna river basin, Western Himalaya: Using Markov Chain and LSTM approaches P. Chauhan et al. 10.1016/j.aiig.2024.100069
- Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments K. Ma et al. 10.1016/j.jhydrol.2024.130841
- Advanced Machine Learning Techniques to Improve Hydrological Prediction: A Comparative Analysis of Streamflow Prediction Models V. Kumar et al. 10.3390/w15142572
- Long-term streamflow forecasting in data-scarce regions: Insightful investigation for leveraging satellite-derived data, Informer architecture, and concurrent fine-tuning transfer learning F. Ghobadi et al. 10.1016/j.jhydrol.2024.130772
- Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods J. Wu et al. 10.1016/j.envres.2023.117272
- Application of machine learning models in assessing the hydrological changes under climate change in the transboundary 3S River Basin Q. Nguyen et al. 10.2166/wcc.2023.313
- Futuristic Streamflow Prediction Based on CMIP6 Scenarios Using Machine Learning Models B. Ullah et al. 10.1007/s11269-023-03645-3
- Divergent path: isolating land use and climate change impact on river runoff S. Mahmood et al. 10.3389/fenvs.2024.1338512
- Boosting algorithms for projecting streamflow in the Lower Godavari Basin for different climate change scenarios B. Mishra et al. 10.2166/wst.2024.011
- Identification of Time-Varying Conceptual Hydrological Model Parameters with Differentiable Parameter Learning X. Lian et al. 10.3390/w16060896
- Response of the Northwest Indian Ocean purpleback flying squid (Sthenoteuthis oualaniensis) fishing grounds to marine environmental changes and its prediction model construction based on multi-models and multi-spatial and temporal scales H. Han et al. 10.1016/j.ecolind.2023.110809
- Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective M. Khan et al. 10.2166/wst.2023.340
- GTWS-MLrec: global terrestrial water storage reconstruction by machine learning from 1940 to present J. Yin et al. 10.5194/essd-15-5597-2023
- Runoff Forecasting of Machine Learning Model Based on Selective Ensemble S. Liu et al. 10.1007/s11269-023-03566-1
- Improved monthly streamflow prediction using integrated multivariate adaptive regression spline with K-means clustering: implementation of reanalyzed remote sensing data O. Kisi et al. 10.1007/s00477-024-02692-5
- Hybrid multi-model ensemble learning for reconstructing gridded runoff of Europe for 500 years U. Singh et al. 10.1016/j.inffus.2023.101807
- Performance evaluation of different gridded precipitation and CMIP6 model products with gauge observations for assessing rainfall variability under the historical and future climate change scenario over a semi-arid catchment, India D. Chatterjee et al. 10.1016/j.pce.2023.103433
- Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects B. Li et al. 10.1016/j.ejrh.2023.101547
- A novel framework for peak flow estimation in the himalayan river basin by integrating SWAT model with machine learning based approach S. Raaj et al. 10.1007/s12145-023-01163-9
Latest update: 24 Apr 2024
Short summary
This study examines, for the first time, the potential of various machine learning models in streamflow prediction over the Sutlej River basin (rainfall-dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between the 2050s and 2080s by 0.79 to 1.43 % for SSP585 and by 0.87 to 1.10 % for SSP245.
This study examines, for the first time, the potential of various machine learning models in...