Sequential Imputation of Missing Spatio-Temporal Precipitation Data Using Random Forests
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Scott L. Painter | Dipankar Dwivedi | James B. Brown | Boris Faybishenko | Utkarsh Mital | James B. Brown | Carl I. Steefel | C. Steefel | B. Faybishenko | D. Dwivedi | U. Mital | S. Painter
[1] M. Islam,et al. Comparison of missing value estimation techniques in rainfall data of Bangladesh , 2018, Theoretical and Applied Climatology.
[2] Lei Chen,et al. Comparison of the multiple imputation approaches for imputing rainfall data series and their applications to watershed models , 2019, Journal of Hydrology.
[3] Susan S. Hubbard,et al. Challenges in Building an End-to-End System for Acquisition, Management, and Integration of Diverse Data From Sensor Networks in Watersheds: Lessons From a Mountainous Community Observatory in East River, Colorado , 2019, IEEE Access.
[4] Y. Pachepsky,et al. Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. , 2010 .
[5] J. Banfield,et al. The East River, Colorado, Watershed: A Mountainous Community Testbed for Improving Predictive Understanding of Multiscale Hydrological–Biogeochemical Dynamics , 2018 .
[6] Hoshin Vijai Gupta,et al. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling , 2009 .
[7] G. S. Dwarakish,et al. A Review on Hydrological Models , 2015 .
[8] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[9] Emanuele Barca,et al. A methodology for treating missing data applied to daily rainfall data in the Candelaro River Basin (Italy) , 2010, Environmental monitoring and assessment.
[10] Mark R. Segal,et al. Machine Learning Benchmarks and Random Forest Regression , 2004 .
[11] D. Dwivedi,et al. Impact of Intra-meander Hyporheic Flow on Nitrogen Cycling , 2017 .
[12] Jorge Luis Morales,et al. Analysis of a new spatial interpolation weighting method to estimate missing data applied to rainfall records , 2019, Atmósfera.
[13] R. Webster,et al. Basic Steps in Geostatistics: The Variogram and Kriging , 2015, SpringerBriefs in Agriculture.
[14] T. Schneider. Analysis of Incomplete Climate Data: Estimation of Mean Values and Covariance Matrices and Imputation of Missing Values. , 2001 .
[15] J. Schafer,et al. Missing data: our view of the state of the art. , 2002, Psychological methods.
[16] Guillaume Favreau,et al. AMMA‐CATCH, a Critical Zone Observatory in West Africa Monitoring a Region in Transition , 2018 .
[17] Jonathan D. Cryer,et al. Time Series Analysis , 1986 .
[18] Xuebin Zhang,et al. Trends in Total Precipitation and Frequency of Daily Precipitation Extremes over China , 2005 .
[19] P. Shuai,et al. Kilometer‐Scale Hydrologic Exchange Flows in a Gravel Bed River Corridor and Their Implications to Solute Migration , 2020, Water Resources Research.
[20] C. Daly,et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States , 2008 .
[21] Sayang Mohd Deni,et al. The Effectiveness of a Probabilistic Principal Component Analysis Model and Expectation Maximisation Algorithm in Treating Missing Daily Rainfall Data , 2019, Asia-Pacific Journal of Atmospheric Sciences.
[22] J. Gómez-Camacho,et al. A novel approach to precipitation series completion in climatological datasets: application to Andalusia , 2008 .
[23] S. Hubbard,et al. Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry , 2020, Hydrological Processes.
[24] M. C. Acock,et al. Estimating Missing Weather Data for Agricultural Simulations Using Group Method of Data Handling , 2000 .
[25] Shreenivas Londhe,et al. Infilling of missing daily rainfall records using artificial neural network , 2015 .
[26] Cem Iyigun,et al. Comparison of missing value imputation methods in time series: the case of Turkish meteorological data , 2013, Theoretical and Applied Climatology.
[27] Ramesh S. V. Teegavarapu,et al. Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records , 2005 .
[28] D. Shepard. A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.
[29] M. Maugeri,et al. Improving estimation of missing values in daily precipitation series by a probability density function‐preserving approach , 2010 .
[30] Paulin Coulibaly,et al. Comparison of neural network methods for infilling missing daily weather records , 2007 .
[31] R. Teegavarapu. Precipitation imputation with probability space-based weighting methods , 2020 .
[32] Gunnar Lischeid,et al. A review on missing hydrological data processing , 2018, Environmental Earth Sciences.
[33] Andrey Gorshenin,et al. Application of Machine Learning Algorithms to Handle Missing Values in Precipitation Data , 2019, DCCN.
[34] C. Steefel,et al. Hot Spots and Hot Moments of Nitrogen in a Riparian Corridor , 2018 .
[35] Fei Tang,et al. Random forest missing data algorithms , 2017, Stat. Anal. Data Min..
[36] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[37] Mathieu Vrac,et al. Comparison of statistical downscaling methods with respect to extreme events over Europe: Validation results from the perfect predictor experiment of the COST Action VALUE , 2019 .
[38] Dipankar Dwivedi,et al. Detecting control system misbehavior by fingerprinting programmable logic controller functionality , 2019, Int. J. Crit. Infrastructure Prot..
[39] Gilles Louppe,et al. Understanding Random Forests: From Theory to Practice , 2014, 1407.7502.
[40] Mahsa Hasanpour Kashani,et al. Evaluation of efficiency of different estimation methods for missing climatological data , 2011, Stochastic Environmental Research and Risk Assessment.
[41] M. A. Kohler,et al. INTERPOLATION OF MISSING PRECIPITATION RECORDS , 1952 .
[42] Jeffrey G. Arnold,et al. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .
[43] Yacine Rezgui,et al. Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption , 2017 .