Aus der Einleitung:
The calculation and prediction of river flow is a very old problem. Especially extremely high values of the runoff can cause enormous economic damage. A system which precisely predicts the runoff and warns in case of a flood event can prevent a high amount of the damages.
On the basis of a good flood forecast, one can take action by preventive methods and warnings. An efficient constructional flood retention can reduce the effects of a flood event enormously.With a precise runoff prediction with longer lead times (>48h), the dam administration is enabled to give order to their gatekeepers to empty dams and reservoirs very fast, following a smart strategy. With a good timing, that enables the dams later to store and retain the peak of the flood and to reduce all effects of damage in the downstream. A warning of people in possible flooded areas with greater lead time, enables them to evacuate not fixed things like cars, computers, important documents and so on. Additionally it is possible to use the underlying rainfall-runoff model to perform runoff simulations to find out which areas are threatened at which precipitation events and associated runoff in the river. Altogether these methods can avoid a huge amount of economic damage.:List of Symbols and Abbreviations S. III
1 Introduction S. 1
2 Process based Rainfall-Runoff Modelling S. 5
2.1 Basics of runoff processes S. 5
2.2 Physically based rainfall-runoff and hydrodynamic river models S. 15
3 Portraying Rainfall-Runoff Processes with Neural Networks S. 21
3.1 The Challenge in General S. 22
3.2 State-of-the-art Approaches S. 24
3.3 Architectures of neural networks for time series prediction S. 26
4 Requirements specification S. 33
5 The PAI-OFF approach as the base of the system S. 35
5.1 Pre-Processing of the Input Data S. 37
5.2 Operating and training the PoNN S. 47
5.3 The PAI-OFF approach - an Intelligent System S. 52
6 Design and Implementation S. 55
6.1 Design S. 55
6.2 Implementation S. 58
6.3 Exported interface definition S. 62
6.4 Displaying output data with involvement of uncertainty S. 64
7 Results and Discussion S. 69
7.1 Evaluation of the Results S. 69
7.2 Discussion of the achieved state S. 75
8 Conclusion and FutureWork S. 77
8.1 Access to real-time meteorological input data S. 77
8.2 Using further developed prediction methods S. 79
8.3 Development of a graphical user interface S. 80
Bibliography S. 83
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:26441 |
| Date | January 2007 |
| Creators | Krauße, Thomas |
| Publisher | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:workingPaper, info:eu-repo/semantics/workingPaper, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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