Date of Award:

8-2011

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Wynn Walker (Committee Co-Chair), Mac McKee (Committee Co-Chair)

Committee

Wynn Walker

Committee

Mac McKee

Committee

Gary Merkley

Committee

David Stevens

Committee

Gilberto Urroz

Abstract

A crucial decision in the real-time management of today’s irrigation systems involves the coordination of diversions and delivery of water to croplands. Since most irrigation systems experience significant lags between when water is diverted and when it should be delivered, an important technical innovation in the next few years will involve improvements in short-term irrigation demand forecasting.

The main objective of the researches presented was the development of these critically important models: (1) potential evapotranspiration forecasting; (2) hydraulic model error correction; and (3) estimation of aggregate water demands. These tools are based on statistical machine learning or data-driven modeling. These, of wide application in several areas of engineering analysis, can be used in irrigation and system management to provide improved and timely information to water managers. The development of such models is based on a Bayesian data-driven algorithm called the Relevance Vector Machine (RVM), and an extension of it, the Multivariate Relevance Vector Machine (MVRVM). The use of these types of learning machines has the advantage of avoidance of model overfitting, high robustness in the presence of unseen data, and uncertainty estimation for the results (error bars).

The models were applied in an irrigation system located in the Lower Sevier River Basin near Delta, Utah.

For the first model, the proposed method allows for estimation of future crop water demand values up to four days in advance. The model uses only daily air temperatures and the MVRVM as mapping algorithm.

The second model minimizes the lumped error occurring in hydraulic simulation models. The RVM is applied as an error modeler, providing estimations of the occurring errors during the simulation runs.

The third model provides estimation of future water releases for an entire agricultural area based on local data and satellite imagery up to two days in advance.

The results obtained indicate the excellent adequacy in terms of accuracy, robustness, and stability, especially in the presence of unseen data. The comparison provided against another data-driven algorithm, of wide use in engineering, the Multilayer Perceptron, further validates the adequacy of use of the RVM and MVRVM for these types of processes.

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Comments

This work made publicly available electronically on August 4, 2011.

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