Application of the relevance vector machine to canal flow prediction in the Sevier River Basin
Section snippets
Introduction and background
One of the biggest challenges in areas with limited water is getting the necessary amounts of water to the desired places at the appropriate times, with the ever-present objective of providing the water with minimal loss in transmission and minimal excess. Meeting this challenge is problematic when, as is often the case, the amounts of water needed, the locations and times of need, and the losses that will occur are not precisely known at the time when water management and diversion decisions
Predictive function estimation and the relevance vector machine
In this section we very briefly introduce the relevance vector machine to establish some notation and concepts for following discourse. A considerably more complete discussion and derivation of the results appears in Flake (2007); the relevance vector machine appears initially in Tipping (2001).
Prediction is the deduction or estimation of a system condition based on some functional or intuitive relationship between that condition and other conditions in the system. The task of machine learning
Application to canal flows in the Sevier River Basin
For initial assessment of potential model inputs and in much of our experimentation we used flow data from the Richfield Canal, one of the largest canals in the Sevier River Basin. An example of hourly flow measurements taken at the head of Richfield Canal is shown in Fig. 1. The figure shows significant periods of flow during the growing season separated by short periods of zero or near-zero flow. This interrupted flow pattern is indicative of the water requirements of the major crops
Summary and conclusions
We have established a method for predicting canal flow for canals in the Sevier River Basin.
Through our experiments it has become apparent that the water use of farmers may not reflect well the crop water need, especially with the limited serviceability of the evapotranspiration input. It is disputable that the prediction models formed are more evidence of the RVM learning patterns of farmer and canal operator behavior than of a system with physical input–output implications. In this way,
References (9)
- Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration—guidelines for computing crop water...
- et al.
Developing a virtual watershed: The Sevier river basin
- Berger, B., Hansen, R., Jensen, R., 2003. Sevier river basin system description. Tech. rep., Sevier River Water Users...
- Flake, J.T., 2007. Application of the relevance vector machine to canal flow prediction in the Sevier river basin....
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