Articles | Volume 22, issue 12
https://doi.org/10.5194/nhess-22-3859-2022
https://doi.org/10.5194/nhess-22-3859-2022
Research article
 | 
01 Dec 2022
Research article |  | 01 Dec 2022

Comparison of machine learning techniques for reservoir outflow forecasting

Orlando García-Feal, José González-Cao, Diego Fernández-Nóvoa, Gonzalo Astray Dopazo, and Moncho Gómez-Gesteira

Related authors

How to mitigate flood events similar to the 1979 catastrophic floods in the lower Tagus
Diego Fernández-Nóvoa, Alexandre M. Ramos, José González-Cao, Orlando García-Feal, Cristina Catita, Moncho Gómez-Gesteira, and Ricardo M. Trigo
Nat. Hazards Earth Syst. Sci., 24, 609–630, https://doi.org/10.5194/nhess-24-609-2024,https://doi.org/10.5194/nhess-24-609-2024, 2024
Short summary
Multiscale flood risk assessment under climate change: the case of the Miño River in the city of Ourense, Spain
Diego Fernández-Nóvoa, Orlando García-Feal, José González-Cao, Maite deCastro, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 22, 3957–3972, https://doi.org/10.5194/nhess-22-3957-2022,https://doi.org/10.5194/nhess-22-3957-2022, 2022
Short summary
Towards an automatic early warning system of flood hazards based on precipitation forecast: the case of the Miño River (NW Spain)
José González-Cao, Orlando García-Feal, Diego Fernández-Nóvoa, José Manuel Domínguez-Alonso, and Moncho Gómez-Gesteira
Nat. Hazards Earth Syst. Sci., 19, 2583–2595, https://doi.org/10.5194/nhess-19-2583-2019,https://doi.org/10.5194/nhess-19-2583-2019, 2019
Short summary

Related subject area

Databases, GIS, Remote Sensing, Early Warning Systems and Monitoring Technologies
Prediction of the volume of shallow landslides due to rainfall using data-driven models
Jérémie Tuganishuri, Chan-Young Yune, Gihong Kim, Seung Woo Lee, Manik Das Adhikari, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci., 25, 1481–1499, https://doi.org/10.5194/nhess-25-1481-2025,https://doi.org/10.5194/nhess-25-1481-2025, 2025
Short summary
Monitoring snow depth variations in an avalanche release area using low-cost lidar and optical sensors
Pia Ruttner, Annelies Voordendag, Thierry Hartmann, Julia Glaus, Andreas Wieser, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 25, 1315–1330, https://doi.org/10.5194/nhess-25-1315-2025,https://doi.org/10.5194/nhess-25-1315-2025, 2025
Short summary
Satellite-based data for agricultural index insurance: a systematic quantitative literature review
Thuy T. Nguyen, Shahbaz Mushtaq, Jarrod Kath, Thong Nguyen-Huy, and Louis Reymondin
Nat. Hazards Earth Syst. Sci., 25, 913–927, https://doi.org/10.5194/nhess-25-913-2025,https://doi.org/10.5194/nhess-25-913-2025, 2025
Short summary
A methodology to compile multi-hazard interrelationships in a data-scarce setting: an application to the Kathmandu Valley, Nepal
Harriet E. Thompson, Joel C. Gill, Robert Šakić Trogrlić, Faith E. Taylor, and Bruce D. Malamud
Nat. Hazards Earth Syst. Sci., 25, 353–381, https://doi.org/10.5194/nhess-25-353-2025,https://doi.org/10.5194/nhess-25-353-2025, 2025
Short summary
An automated approach for developing geohazard inventories using news: Integrating NLP, machine learning, and mapping
Aydoğan Avcıoğlu, Ogün Demir, and Tolga Görüm
EGUsphere, https://doi.org/10.5194/egusphere-2025-7,https://doi.org/10.5194/egusphere-2025-7, 2025
Short summary

Cited articles

Adaramola, M.: Climate Change And The Future Of Sustainability: The Impact on Renewable Resources, CRC Press, 1–336, https://doi.org/10.1201/9781315366050, 2016. 
Alcamo, J., Dronin, N., Endejan, M., Golubev, G., and Kirilenko, A.: A new assessment of climate change impacts on food production shortfalls and water availability in Russia, Global Environ. Change, 17, 429–444, https://doi.org/10.1016/j.gloenvcha.2006.12.006, 2007. 
Amirkhani, S., Tootchi, A., and Chaibakhsh, A.: Fault detection and isolation of gas turbine using series–parallel NARX model, ISA Trans., 120, 205–221, https://doi.org/10.1016/j.isatra.2021.03.019, 2022. 
Arnell, N. W. and Gosling, S. N.: The impacts of climate change on river flood risk at the global scale, Climatic Change, 134, 387–401, https://doi.org/10.1007/s10584-014-1084-5, 2016. 
Baba, A., Tsatsanifos, C., el Gohary, F., Palerm, J., Khan, S., Mahmoudian, S. A., Ahmed, A. T., Tayfur, G., Dialynas, Y. G., and Angelakis, A. N.: Developments in water dams and water harvesting systems throughout history in different civilizations, Int. J. Hydrol., 2, 155–171, https://doi.org/10.15406/ijh.2018.02.00064, 2018. 
Download
Short summary
Extreme events have increased in the last few decades; having a good estimation of the outflow of a reservoir can be an advantage for water management or early warning systems. This study analyzes the efficiency of different machine learning techniques to predict reservoir outflow. The results obtained showed that the proposed models provided a good estimation of the outflow of the reservoirs, improving the results obtained with classical approaches.
Share
Altmetrics
Final-revised paper
Preprint