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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Application of the Relevance Vector Machine to Canal Flow Prediction in the Sevier River Basin

Flake, John T. 01 May 2007 (has links)
This work addresses management of the scarce water resource for irrigation in arid regions where significant delays between the time of order and the time of delivery present major difficulties. Motivated by improvements to water management that will be facilitated by an ability to predict water demand, this work employs a data-driven approach to developing canal flow prediction models using the Relevance Vector Machine (RVM), a probabilistic kernel-based learning machine. Beyond the RVM learning process, which establishes the set of relevant vectors from the training data, a search is performed across model attributes including input set, kernel scale parameter, and model update scheme for models providing superior prediction capability. Models are developed for two canals in the Sevier River Basin of southern Utah for prediction horizons of up to five days. Appendices provide the RVM derivation in detail.
2

Comparison of behaviour of 1520 mm (60 in.) concrete pipe with sidd design under deep cover

Haque, Md. Mominul January 1998 (has links)
No description available.

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