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Williams, Peter M (1998) Modelling Seasonality and Trends in Daily Rainfall Data. In: Advances in Neural Information Processing Systems.
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Official URL: http://books.nips.cc/papers/files/nips10/0985.pdf
Abstract
This paper presents a new approach to modelling daily rainfall using neural networks. We fist model the conditional distributions of rainfall amounts, in such a way that the model itself determines the order of the process, and the time-dependent shape and scale of the conditional distributions. After integrating over particular weather patterns, we are able to extracxt seasonal variations and long-term trends.
Item Type: | Conference or Workshop Item (Paper) |
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Schools and Departments: | School of Engineering and Informatics > Informatics |
Depositing User: | EPrints Services |
Date Deposited: | 06 Feb 2012 20:05 |
Last Modified: | 07 Jun 2012 14:43 |
URI: | http://sro.sussex.ac.uk/id/eprint/23941 |