A non-linear neural network technique for updating of river flow forecasts

Hydrology and Earth System Sciences

Volumn 5, Issue 4, 2001, Pages 577-597

  Shamseldin, Asaad Y ^a   O'Connor, Kieran M ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Auto regressive exogenous input model; Neural network; Out updating procedure; Soil moisture accounting and routing (SMAR) model

Indexed keywords

DISCHARGE RATE; FLOW ANALYSIS; FORECASTING; NEURAL NETWORK; RIVER;

ARTIFICIAL NEURAL NETWORK; FORECASTING METHOD; RIVER DISCHARGE; RIVER FLOW;

EID: 0035701248     PISSN: 10275606     EISSN: None     Source Type: Journal    
DOI: 10.5194/hess-5-577-2001     Document Type: Article

References (78)

1. A simple non-linear rainfall-runoff model with a variable gain factor
2. Determination of model order for NARX models directly from input-output data
3. A neural network approach to identifying non-point sources of microbial contamination
4. Evaluation of global optimization methods for conceptual rainfall-runoff model calibration
5. Comment on "The use of artificial neural networks for the prediction of water quality parameters
6. Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoff models
7. A comparison of parametric and non-parametric methods for runoff forecasting
8. Effects of model complexity and structure, data quality, and objective functions on hydrologic modelling
9. Working with neural networks
10. Assessment criteria for simple conceptual daily rainfall-runoff models
11. A unit hydrograph rainfall-runoff model using Mathematica
12. Artificial neural network modelling of the rainfall-runoff process
13. A modified spatial soil moisture storage capacity distribution curve for the Xinanjiang model
14. River Flow forecasting. Part 5. Applications of a conceptual model
15. A hydrological perspective
16. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm
17. Discharge and water table predictions using a generalized TOPMODEL formulation
18. New mathematical approaches in hydrological modelling - An application of artificial neural networks. Physics and Chemistry of the Earth
19. Comparison of two procedures for improving short-term forecasts of the natural flows in a deterministic model
20. An introduction to computing with neural nets
21. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting
22. The use of artificial neural networks for the prediction of water quality parameters
23. Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications
24. Artificial neural network modelling as rainfall-runoff model
25. Real-time flood forecasting systems: Perspective and prospects
26. The quest for more powerful validation of conceptual catchment models
27. River flow forecasting through conceptual models Part. 1. A discussion of principles
28. The form of the instantaneous unit hydrograph
29. A simplex method for function optimization
30. Adaptive hydrological forecasting- A review
31. River flow forecasting through conceptual models. Part 2. The Brosna catchment at Ferbane
32. Identification of the rainfall-runoff relationship in urban drainage networks
33. Validation and intercomparison of different updating procedures for real-time forecasting
34. An automatic method for finding the greatest or least value of a function
35. Multiscale calibration and validation of a conceptual rainfall-runoff model. Physics and Chemistry of the Earth
36. Hydrological forecasting and updating procedures
37. A real-time combination method for the outputs of different rainfall-runoff models
38. Real-Time flood forecasting on the Blue Nile river using the SMAR-AR model
39. Methods for combining the outputs of different rainfall-runoff models
40. Calibration of a modified SFB model for twenty-five Australian catchments using simulated annealing
41. Application of an empirical equation in the SMAR model conceptual model
42. Criteria for evaluation of watershed models
43. River stage forecasting using artificial neural networks
44. Probabilistic optimization for conceptual rainfall-runoff models: A comparison of the shuffled complex evolution and simulated annealing algorithms
45. The ARNO rainfall-runoff model: Past present and future
46. The genetic algorithm and its application to calibrating conceptual rainfall-runoff models
47. Backpropogation through time: What it does and how to do it
48. Effects of spatial variability and scale with implications to hydrological modelling
49. Automatic calibration of conceptual rainfall-runoff models: Sensitivity to calibration data
50. Performance of conceptual rainfall-runoff models in low-yielding ephemeral catchments
51. Neural model input selection for a MIMO chemical process
52. Unit hydrographs to model quick and slow runoff components of streamflow