A model that includes both ®rst principles dierential equations and an arti®cial neural network is used to forecast and control an environmental process. The inclusion of the ®rst principles knowledge in this hybrid model is shown to improve substantially the stability of the model predictions in sp
✦ LIBER ✦
RANDOM PROCESS METHODS AND ENVIRONMENTAL DATA: THE 1996 HUNTER LECTURE
✍ Scribed by DAVID R. BRILLINGER
- Publisher
- John Wiley and Sons
- Year
- 1997
- Tongue
- English
- Weight
- 210 KB
- Volume
- 8
- Category
- Article
- ISSN
- 1180-4009
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✦ Synopsis
Random processes are basic to the study of environmental data, particularly data in time and space. This work presents three data analyses based on random process models: (a) a trend analysis, based on ®tting a monotonic trend to river heights; (b) an analysis of point process data, with ordinal-valued marks, for damage assessment following an earthquake, and (c) an analysis of spatial-temporal meteorological data to estimate the speed of motion of a 500 mbar surface. There is discussion of stochastic processes generally.
📜 SIMILAR VOLUMES
Hybrid neural network models for environ
✍
Richard D. De Veaux; Rod Bain; Lyle H. Ungar
📂
Article
📅
1999
🏛
John Wiley and Sons
🌐
English
⚖ 132 KB
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