Berga, L.: The Role of Hydropower in Climate Change Mitigation and Adaptation: A Review, Engineering, 2, 313–318, https://doi.org/10.1016/J.ENG.2016.03.004, 2016.
Berghuijs, W. R., Aalbers, E. E., Larsen, J. R., Trancoso, R., and Woods, R.
A.: Recent changes in extreme floods across multiple continents, Environ. Res. Lett., 12, 114035, https://doi.org/10.1088/1748-9326/aa8847, 2017.
Booth, D. B. and Bledsoe, B. P.: Streams and urbanization, in: The Water
Environment of Cities, edited by: Baker, L. A., Springer US, Boston, MA,
93–123, https://doi.org/10.1007/978-0-387-84891-4_6, 2009.
Bradshaw, C. J. A., Sodhi, N. S., Peh, K. S. H., and Brook, B. W.: Global
evidence that deforestation amplifies flood risk and severity in the developing world, Global Change Biol., 13, 2379–2395,
https://doi.org/10.1111/j.1365-2486.2007.01446.x, 2007.
Castelletti, A., Pianosi, F., and Soncini-Sessa, R.: Water reservoir control
under economic, social and environmental constraints, Automatica, 44,
1595–1607, https://doi.org/10.1016/j.automatica.2008.03.003, 2008.
Confederación Hidrográfica del Miño-Sil: Plan hidrológico de
la parte española de la Demarcación Hidrográfica del
Miño-Sil, 2015–2021,
https://www.chminosil.es/images/planificacion/proyecto-ph-2015-2021-vca/DOCUMENTO_DE_SINTESIS.pdf
(last access: 29 November 2022), 2016.
de la Paix, M. J., Lanhai, L., Xi, C., Ahmed, S., and Varenyam, A.: Soil
degradation and altered flood risk as a consequence of deforestation, Land
Degrad. Dev., 24, 478–485, https://doi.org/10.1002/ldr.1147, 2013.
Dozat, T.: Incorporating Nesterov Momentum into Adam, in: ICLR Workshop,
2–4 May 2016, San Juan, Puerto Rico, 2013–2016,
https://openreview.net/forum?id=OM0jvwB8jIp57ZJjtNEZ (last access: 29 November 2022), 2016.
Elliott, J., Deryng, D., Müller, C., Frieler, K., Konzmann, M., Gerten,
D., Glotter, M., Flörke, M., Wada, Y., Best, N., Eisner, S., Fekete, B.
M., Folberth, C., Foster, I., Gosling, S. N., Haddeland, I., Khabarov, N.,
Ludwig, F., Masaki, Y., Olin, S., Rosenzweig, C., Ruane, A. C., Satoh, Y.,
Schmid, E., Stacke, T., Tang, Q., and Wisser, D.: Constraints and potentials
of future irrigation water availability on agricultural production under
climate change, P. Natl. Acad. Sci. USA, 111, 3239–3244, https://doi.org/10.1073/pnas.1222474110, 2014.
Emami, S. and Parsa, J.: Comparative evaluation of imperialist competitive
algorithm and artificial neural networks for estimation of reservoirs storage capacity, Appl. Water Sci., 10, 177, https://doi.org/10.1007/s13201-020-01259-3, 2020.
Farizawani, A., Puteh, M., Marina, Y., and Rivaie, A.: A review of artificial neural network learning rule based on multiple variant of conjugate gradient approaches, J. Phys.: Conf. Ser., 1529, 022–040, https://doi.org/10.1088/1742-6596/1529/2/022040, 2020.
Fernández-Nóvoa, D., deCastro, M., Des, M., Costoya, X., Mendes, R.,
and Gómez-Gesteira, M.: Characterization of Iberian turbid plumes by
means of synoptic patterns obtained through MODIS imagery, J. Sea Res., 126, 12–25, https://doi.org/10.1016/j.seares.2017.06.013, 2017.
Field, C. B., Barros, V., Stocker, T. F., Dahe, Q., Jon Dokken, D., Ebi, K.
L., Mastrandrea, M. D., Mach, K. J., Plattner, G. K., Allen, S. K., Tignor,
M., and Midgley, P. M.: Managing the risks of extreme events and disasters
to advance climate change adaptation: Special report of the intergovernmental panel on climate change, Cambridge University Press, 1–582, https://doi.org/10.1017/CBO9781139177245, 2012.
Géron, A.: Hands-on machine learning with Scikit-Learn, Keras and
TensorFlow: concepts, tools, and techniques to build intelligent systems,
O'Reilly Media, Inc., 851 pp., ISBN 9781492032649, 2019.
Ghorbani, M. A., Deo, R. C., Karimi, V., Kashani, M. H., and Ghorbani, S.:
Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level
forecasting, Stoch. Environ. Res. Risk A., 33, 125–147, https://doi.org/10.1007/s00477-018-1630-1, 2019.
Guzman, S. M., Paz, J. O., and Tagert, M. L. M.: The Use of NARX Neural Networks to Forecast Daily Groundwater Levels, Water Resour. Manage., 31, 1591–1603, https://doi.org/10.1007/s11269-017-1598-5, 2017.
Hallegatte, S.: A Cost Effective Solution to Reduce Disaster Losses in
Developing Countries: HydroMeteorological Services, Early Warning and Evacuation, World Bank policy research paper No. 6058, The World Bank, Washington, DC, https://doi.org/10.1596/1813-9450-6058, 2012.
Jeuland, M., Baker, J., Bartlett, R., and Lacombe, G.: The costs of uncoordinated infrastructure management in multi-reservoir river basins,
Environ. Res. Lett., 9, 105006, https://doi.org/10.1088/1748-9326/9/10/105006, 2014.
Jonkman, S. N.: Global perspectives on loss of human life caused by floods,
Nat. Hazards, 34, 151–175, https://doi.org/10.1007/s11069-004-8891-3, 2005.
Kingma, D. P. and Ba, J.: Adam: A Method for Stochastic Optimization,
ARXIV: preprint, https://doi.org/10.48550/ARXIV.1412.6980, 2014.
Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.:
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks,
Hydrol. Earth Syst. Sci., 22, 6005–6022, https://doi.org/10.5194/hess-22-6005-2018, 2018.
Le, X. H., Ho, H. V., Lee, G., and Jung, S.: Application of Long Short-Term
Memory (LSTM) neural network for flood forecasting, Water, 11, 1387, https://doi.org/10.3390/w11071387, 2019.
Lee, S.-Y., Hamlet, A. F., Fitzgerald, C. J., and Burges, S. J.: Optimized
Flood Control in the Columbia River Basin for a Global Warming Scenario, J. Water Resour. Plan. Manage., 135, 440–450, https://doi.org/10.1061/(asce)0733-9496(2009)135:6(440), 2009.
Lee, W. K. and Tuan Resdi, T. A.: Simultaneous hydrological prediction at
multiple gauging stations using the NARX network for Kemaman catchment,
Terengganu, Malaysia, Hydrolog. Sci. J., 61, 2930–2945, https://doi.org/10.1080/02626667.2016.1174333, 2016.
Lin, T., Horne, B. G., Tiňo, P., and Giles, C. L.: Learning long-term
dependencies in NARX recurrent neural networks, IEEE T. Neural Netw., 7, 1329–1338, https://doi.org/10.1109/72.548162, 1996.
Liu, C., Guo, L., Ye, L., Zhang, S., Zhao, Y., and Song, T.: A review of
advances in China's flash flood early-warning system, Nat. Hazards, 92,
619–634, https://doi.org/10.1007/s11069-018-3173-7, 2018.
Livingstone, D. J., Manallack, D. T., and Tetko, I. v.: Data modelling with
neural networks: Advantages and limitations, J. Comput.-Aid. Molec. Design, 11, 135–142, https://doi.org/10.1023/A:1008074223811, 1997.
Markham, I. S. and Rakes, T. R.: The effect of sample size and variability
of data on the comparative performance of artificial neural networks and
regression, Comput. Operat. Res., 25, 251–263, https://doi.org/10.1016/S0305-0548(97)00074-9, 1998.
Marques, G. F. and Tilmant, A.: The economic value of coordination in large-scale multireservoir systems: The Parana River case, Water Resour. Res., 49, 7546–7557, https://doi.org/10.1002/2013WR013679, 2013.
Masters, D. and Luschi, C.: Revisiting Small Batch Training for Deep Neural
Networks, ARXIV: preprint, https://doi.org/10.48550/ARXIV.1804.07612, 2018.
MathWorks Inc.: Design Time Series NARX Feedback Neural Networks – MATLAB & Simulink,
https://es.mathworks.com/help/deeplearning/ug/design-time-series-narx-feedback-neural-networks.html,
last access: 29 November 2022.
Moriasi, D. N., Arnold, J. G., van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L.: Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations, T. ASABE, 50, 885–900, https://doi.org/10.13031/2013.23153, 2007.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
O'Malley, T., Bursztein, E., Long, J., Chollet, F., Jin, H., Invernizzi, L.,
and others: KerasTuner, GitHub [software],
https://github.com/keras-team/keras-tuner (last access: 29 November 2022), 2019.
Passerotti, G., Massazza, G., Pezzoli, A., Bigi, V., Zsótér, E., and
Rosso, M.: Hydrological model application in the Sirba river: Early warning
system and GloFAS improvements, Water, 12, 620, https://doi.org/10.3390/w12030620, 2020.
Quinn, J. D., Reed, P. M., Giuliani, M., and Castelletti, A.: What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir
Operating Policies Using Time-Varying Sensitivity Analysis, Water Resour. Res., 55, 5962–5984, https://doi.org/10.1029/2018WR024177, 2019.
RapidMiner Inc.: Neural Net – RapidMiner Documentation,
https://docs.rapidminer.com/latest/studio/operators/modeling/predictive/neural_nets/neural_net.html,
last access: 29 November 2022.
Rashidi, H. H., Tran, N. K., Betts, E. V., Howell, L. P., and Green, R.:
Artificial Intelligence and Machine Learning in Pathology: The Present
Landscape of Supervised Methods, Academic Pathol., 6, 2374289519873088,
https://doi.org/10.1177/2374289519873088, 2019.
Rjeily, Y. A., Abbas, O., Sadek, M., Shahrour, I., and Chehade, F. H.: Flood
forecasting within urban drainage systems using NARX neural network, Water
Sci. Technol., 76, 2401–2412, https://doi.org/10.2166/wst.2017.409, 2017.
Rosburg, T. T., Nelson, P. A., and Bledsoe, B. P.: Effects of Urbanization
on Flow Duration and Stream Flashiness: A Case Study of Puget Sound Streams,
Western Washington, USA, J. Am. Water Resour. Assoc., 53, 493–507, https://doi.org/10.1111/1752-1688.12511, 2017.
Rougé, C., Reed, P. M., Grogan, D. S., Zuidema, S., Prusevich, A., Glidden, S., Lamontagne, J. R., and Lammers, R. B.: Coordination and control-limits in standard representations of multi-reservoir operations in
hydrological modeling, Hydrol. Earth Syst. Sci., 25, 1365–1388,
https://doi.org/10.5194/hess-25-1365-2021, 2021.
Sammen, S. S., Mohamed, T. A., Ghazali, A. H., El-Shafie, A. H., and Sidek,
L. M.: Generalized Regression Neural Network for Prediction of Peak Outflow
from Dam Breach, Water Resour. Manage., 31, 549–562, https://doi.org/10.1007/s11269-016-1547-8, 2017.
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: Some past experiences and new approaches, J. Hydroinform., 10, 3–22,
https://doi.org/10.2166/hydro.2008.015, 2008.
Sutskever, I., Vinyals, O., and Le, Q. V.: Sequence to Sequence Learning with
Neural Networks, in: Advances in Neural Information Processing Systems,
Curran Associates, Inc., ISBN 9781510800410,
https://proceedings.neurips.cc/paper/2014/file/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf
(last access: 29 November 2022), 2014.
Taghi Sattari, M., Yurekli, K., and Pal, M.: Performance evaluation of
artificial neural network approaches in forecasting reservoir inflow, Appl. Math. Model., 36, 2649–2657, https://doi.org/10.1016/j.apm.2011.09.048, 2012.
Wallemacq, P., House, R., Below, R., and McLean, D.: Economic losses,
poverty & disasters: 1998–2017, Brussels, Belgium,
https://www.undrr.org/publication/economic-losses-poverty-disasters-1998-2017
(last access: 29 November 2022) 2018.
Xiang, Z., Yan, J., and Demir, I.: A Rainfall-Runoff Model With LSTM-Based
Sequence-to-Sequence Learning, Water Resour. Res., 56, e2019WR02532, https://doi.org/10.1029/2019WR025326, 2020.
Xie, H., Tang, H., and Liao, Y. H.: Time series prediction based on narx
neural networks: An advanced approach, in: Proceedings of the 2009 International Conference on Machine Learning and Cybernetics, 12–15 July 2009, Baoding, Hebei, China, 1275–1279, https://doi.org/10.1109/ICMLC.2009.5212326, 2009.
Xiong, W., Conway, D., Lin, E., Xu, Y., Ju, H., Jiang, J., Holman, I., and
Li, Y.: Future cereal production in China: The interaction of climate change, water availability and socio-economic scenarios, Global Environ. Change, 19, 34–44, https://doi.org/10.1016/j.gloenvcha.2008.10.006, 2009.
Yang, S., Yang, D., Chen, J., and Zhao, B.: Real-time reservoir operation
using recurrent neural networks and inflow forecast from a distributed
hydrological model, J. Hydrol., 579, 124229, https://doi.org/10.1016/j.jhydrol.2019.124229, 2019.
Zhang, D., Lin, J., Peng, Q., Wang, D., Yang, T., Sorooshian, S., Liu, X.,
and Zhuang, J.: Modeling and simulating of reservoir operation using the
artificial neural network, support vector regression, deep learning algorithm, J. Hydrol., 565, 720–736, https://doi.org/10.1016/j.jhydrol.2018.08.050, 2018.
