Group-based estimation of missing hydrological data: I. Approach and general methodology

Hydrological Sciences Journal

Volumn 45, Issue 6, 2000, Pages 849-866

  Elshorbagy, Amin A ^a   Panu, U S ^b   Simonovic, S P ^c  

^a UNIVERSITY OF MANITOBA   (Canada)

^b LAKEHEAD UNIVERSITY   (Canada)

^c UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

INTERPOLATION; NEURAL NETWORKS; REGRESSION ANALYSIS; STREAM FLOW; TIME SERIES ANALYSIS; WATER RESOURCES;

GROUP APPROACH; SINGLE VALUED APPROACH;

HYDROLOGY;

DATA PROCESSING; HYDROLOGICAL MODELING; INTERPOLATION; STREAMFLOW;

EID: 0034533222     PISSN: 02626667     EISSN: None     Source Type: Journal    
DOI: 10.1080/02626660009492388     Document Type: Article

References (53)

1. Mixed-station extension of monthly streamflow records
2. Missing values in multivariate analysis
3. Comparison of regression and time series methods for synthesizing missing streamflow records
4. Improving single-variable and multivariable techniques for estimating missing hydrological data
5. Spatial approach to estimation of missing data
6. A spatial time-dependent approach to estimation of hydrologic data
7. The effect of clustering of flood peaks on a flood risk analysis for the Red River
8. An artificial neural network approach to rainfall-runoff modelling
9. Completing missing groundwater observations by interpolation
10. How many clusters are best? An experiment
11. Investigations into group-based data in-filling techniques
12. Group-based estimation of missing hydrological data: II. Application to streamflows
13. On the use of correlation to augment data
14. Rainfall forecasting in space and time using a neural network
15. Reliability of a variance estimate obtained from a sample augmented by multivariate regression
16. Estimating missing values in space-time data series
17. Estimating missing values using neural networks
18. Filling gaps in runoff time series in West Africa
19. An evaluation of some record reconstruction techniques
20. A comparison of four streamflow record extension techniques
21. Artificial neural network modeling of the rainfall-runoff process
22. Daily flow time series patching or extension: A spatial interpolation approach based on flow duration curves
23. Long-term storage capacity of reservoirs
24. Methods of using long-term storage in reservoirs
25. Estimation of missing streamflows: A historical perspective
26. Estimating fluctuation quantities from time series of water-table depths using models with a stochastic component
27. Using artificial neural networks to estimate missing rainfall data
28. Intervention analysis with missing data
29. Patching rainfall data using regression methods. 2. Comparisons of accuracy, bias and efficiency
30. On estimators obtained from a sample augmented by multiple regression
31. Climate data estimation using climate information from surrounding climate stations
32. A feature prediction model in synthetic hydrology based on concepts of pattern recognition
33. Stochastic synthesis of hydrologic data based on concepts of pattern recognition. I. General methodology of the approach
34. Application of some entropic measures in hydrologic data infilling procedures
35. Chapter 12
36. Patching rainfall data using regression methods. 3. Grouping, patching and outlier detection
37. Multivariate modeling of water resources time series using artificial neural networks
38. Approaches to multivariate modelling of water resources time series
39. Analysis and modeling of hydrologic time series
40. Applying soft computing approaches to river level forecasting
41. Forecasting hourly water demands by pattern recognition approach
42. Synthesizing missing streamflow records on several Manitoba streams using multiple nonlinear standardized correlation analysis
43. Disaggregation processes in stochastic hydrology
44. An extension of Box-Jenkins transfer/noise models for spatial interpolation of groundwater head series
45. Minimum variance streamflow record augmentation procedures
46. Short term streamflow forecasting using artificial neural networks
47. Analysis of soil water dynamics in time and space by use of pattern recognition