Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management
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Paola Mercogliano | Arthur H. Essenfelder | Jaroslav Mysiak | Paolo Mazzoli | Stefano Bagli | Davide Broccoli | Valerio Luzzi | Francesca Larosa | Francesco dalla Valle | S. Bagli | J. Mysiak | P. Mercogliano | F. Larosa | P. Mazzoli | A. Essenfelder | V. Luzzi | F. dalla Valle | D. Broccoli
[1] E. Wood,et al. The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting , 2009 .
[2] R. Schaeffer,et al. Energy sector vulnerability to climate change: A review , 2012 .
[3] Enli Wang,et al. Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data , 2011 .
[4] Tamás D. Gedeon,et al. Data Mining of Inputs: Analysing Magnitude and Functional Measures , 1997, Int. J. Neural Syst..
[5] Luis Berga,et al. The Role of Hydropower in Climate Change Mitigation and Adaptation: A Review , 2016 .
[6] T. Palmer,et al. Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts , 2009 .
[7] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[8] Faming Liang,et al. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting , 2011 .
[9] Rob J. Hyndman,et al. A note on the validity of cross-validation for evaluating autoregressive time series prediction , 2018, Comput. Stat. Data Anal..
[10] Rebecca E. Morss,et al. Communicating Uncertainty in Weather Forecasts: A Survey of the U.S. Public , 2008 .
[11] A. Robertson,et al. Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems , 2015 .
[12] Carlo Giupponi,et al. A coupled hydrologic-machine learning modelling framework to support hydrologic modelling in river basins under Interbasin Water Transfer regimes , 2020, Environ. Model. Softw..
[13] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[14] D. C. Sohoulande Djebou. Streamflow Drought Interpreted Using SWAT Model Simulations of Past and Future Hydrologic Scenarios: Application to Neches and Trinity River Basins, Texas , 2019, Journal of Hydrologic Engineering.
[15] Ibrahim Yuksel,et al. Hydropower for sustainable water and energy development , 2010 .
[16] F. Molteni,et al. SEAS5: the new ECMWF seasonal forecast system , 2018, Geoscientific Model Development.
[17] Vijay P. Singh,et al. Seasonal Drought Prediction: Advances, Challenges, and Future Prospects , 2018 .
[18] Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate , 2017 .
[19] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[20] Morten Hjorth-Jensen,et al. Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model , 2020 .
[21] Kiyotaka Tahara,et al. Evaluation of CO2 payback time of power plants by LCA , 1997 .
[22] Mark Z. Jacobson,et al. Review of solutions to global warming, air pollution, and energy security , 2009 .
[23] W. Collischonn,et al. Evaluation of upper Uruguay river basin (Brazil) operational flood forecasts , 2017 .
[24] P. Moorcroft,et al. Impacts of climate change and deforestation on hydropower planning in the Brazilian Amazon , 2020, Nature Sustainability.
[25] Indrajeet Chaubey,et al. Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed , 2008 .
[26] Paul Block,et al. Tailoring seasonal climate forecasts for hydropower operations , 2011 .
[27] Jeffrey G. Arnold,et al. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .
[28] P. Jones,et al. A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006 , 2008 .
[29] S. Bagli,et al. Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results, Advantages, Limits and Lesson Learned , 2017, Water Resources Management.
[30] C. J. Franco,et al. Assessing security of supply in a largely hydroelectricity-based system: The Colombian case , 2018, Energy.
[31] E. Wood,et al. Seasonal Forecasting of Global Hydrologic Extremes: System Development and Evaluation over GEWEX Basins , 2015 .
[32] James A. Edmonds,et al. A Comprehensive View of Global Potential for Hydro-generated Electricity , 2015 .
[33] C. Notarnicola,et al. Seasonal river discharge forecasting using support vector regression: A case study in the Italian Alps , 2015 .
[34] P. D. Heermann,et al. Classification of multispectral remote sensing data using a back-propagation neural network , 1992, IEEE Trans. Geosci. Remote. Sens..
[35] Carlos Jaime Franco,et al. Analyzing the Hydroelectricity Variability on Power Markets from a System Dynamics and Dynamic Systems Perspective: Seasonality and ENSO Phenomenon , 2020 .
[36] Karsten Schulz,et al. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks , 2018, Hydrology and Earth System Sciences.
[37] F. Wolak,et al. Retail Pricing in Colombia to Support the Efficient Deployment of Distributed Generation and Electric Vehicles , 2020, Journal of Environmental Economics and Management.
[38] S. Galelli,et al. Complex relationship between seasonal streamflow forecast skill and value in reservoir operations , 2017 .
[39] Dennis P. Lettenmaier,et al. Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower , 2002 .
[40] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[41] Valentina Krysanova,et al. How the performance of hydrological models relates to credibility of projections under climate change , 2018 .
[42] F. Hossain,et al. Maximizing energy production from hydropower dams using short-term weather forecasts , 2020 .
[43] Bekir Z. Demiray,et al. A comprehensive review of deep learning applications in hydrology and water resources. , 2020, Water science and technology : a journal of the International Association on Water Pollution Research.
[44] Changbo Jiang,et al. Seasonal Inflow Forecasts Using Gridded Precipitation and Soil Moisture Information: Implications for Reservoir Operation , 2019, Water Resources Management.
[45] K. Stone,et al. Climate-driven prediction of land water storage anomalies: An outlook for water resources monitoring across the conterminous United States , 2020 .
[46] Holger R. Maier,et al. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..
[47] Caio A. S. Coelho,et al. Assessment of ECMWF SEAS5 Seasonal Forecast Performance over South America , 2019, Weather and Forecasting.
[48] Jaewook Lee,et al. Clustering Based on Gaussian Processes , 2007, Neural Computation.
[49] A. Soldati,et al. Artificial neural network approach to flood forecasting in the River Arno , 2003 .
[50] Frederic Vitart,et al. Madden—Julian Oscillation prediction and teleconnections in the S2S database , 2017 .
[51] Peggy A. Johnson,et al. Stream hydrological and ecological responses to climate change assessed with an artificial neural network , 1996 .
[52] Ligang Xu,et al. Comparison of Long Short Term Memory Networks and the Hydrological Model in Runoff Simulation , 2020, Water.
[53] Kurt Hornik,et al. kernlab - An S4 Package for Kernel Methods in R , 2004 .
[54] David E.H.J. Gernaat,et al. High-resolution assessment of global technical and economic hydropower potential , 2017 .
[55] Fabio Roli,et al. Design of effective neural network ensembles for image classification purposes , 2001, Image Vis. Comput..
[56] A. Castelletti,et al. The Value of Subseasonal Hydrometeorological Forecasts to Hydropower Operations: How Much Does Preprocessing Matter? , 2019, Water Resources Research.