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.

Fish culture for both food and sport utilization has been greatly increasing throughout the world. The editor of Fish Farming Industries (1973) predicted an increase by 1977 in the United States of 83% for catfish producers, 49% for trout farmers, and 91% for bait dealers. Concomitant with the renewed interest in fish culture has been a corresponding need to further understand fish diseases.

A survey of the mineral resources, the economic rock products, and the ground-water reserves of that part of central Utah drained by the Sevier River system was undertaken by the author in the fall of 1960 as a continuation of a research project directed toward the stiumulation of economic growth in the state of Utah. The project was initiated in 1959 by Dr. Donald R. Olsen and Dr. J. Stewart Williams, who conducted a similar survey of a five county area in southwestern Utah (Olsen and Williams, 1960). Through a review of existing literature, preliminary field examination of most of the important areas, and communications with owners, operators, and consulting geologists, an attempt has been made to include in this survey all of the important economic mineral and rock deposits. A review of the ground-water supplies of the region and a discussion of related problems are also included. Each of the minerals and rock products is described alphabetically in a brief statement. This statement includes information concerning location, present status, present ownership, and geologic controls of accumulation. Where possible an estimate of the economic potential of each commodity is made. These estimates are based on accessibility, tonnage, grade, market value, etc. Each occurrence is also located on a map of the area.

The Sevier River Basin situated in south central Utah is characterized by its semiarid climate, snowmelt-driven runoff, and high dependency on agricultural economy. High evapotranspiration and low precipitation make agricultural production challenging, but naturally stored water in the snowpack in the mountains alleviates water stresses during high water demand seasons. The snowmelt-driven river flow along the main channel is highly exploited for irrigation for farms near the Sevier River. Reservoir operations and river diversions result in heavily regulated flows from the upper to the lower basins. The return flows of over-irrigated water in the upper basin increase salinity of surface water. Long-term applications of salinity water in agriculture eventually produce high soil salinity in the agricultural areas near Delta in the lower basin, which deteriorated farmers’ crop productivity. Farmers cropping near Delta struggle with both water and salinity stresses. Indeed, crop prices and yields are always their concerns. For them, efficient water management can be achieved with consideration of hydrologic, agronomic, and economic aspects of water resources. The overall goal of this research was to develop a decision supporting framework for efficient water and land allocations that considered hydrologic processes, crop response to water in salinity-affected farms, and farmers’ profit and financial risk. This research introduces a methodology for predicting water availability in a given cropping year from the snowpack in the mountains, and agronomic simulations with satellite images follow for quantifying crop response to water. The hydrologic predictions and the agronomic simulations are finally incorporated into an economic analysis that provides efficient water and land allocations with multiple crop selections. In a rural river basin, data limitation is a common concern for water resources engineers; thus simple but robust methodologies are proposed for hydrologic prediction. In the same context, satellite images are used for the estimation of crop yields in individual farms near Delta with no prior crop experimental plots. Historical records of crop prices are used for the economic analysis. The methodologies developed in this research provide a comprehensive decision analysis framework for efficient water management where water is scare and available from snowmelt only, the economy depends on agriculture only, and salinity is present in both soil and water due to long-term irrigation. The case study is for the agricultural area near Delta in the Sevier River Basin, but its applicability is not limited and is flexibly applicable to other agricultural regions.

This thesis develops a new abstraction for solving problems in decision automation. Decision automation is the process of creating algorithms which use data to make decisions without the need for human intervention. In this abstraction, four key ideas/problems are highlighted which must be considered when solving any decision problem. These four problems are the decision problem, the learning problem, the model reduction problem, and the verification problem. One of the benefits of this abstraction is that a wide range of decision problems from many different areas can be broken down into these four “key” sub-problems. By focusing on these key sub-problems and the interactions between them, one can systematically arrive at a solution to the original problem. Three new learning platforms have been developed in the areas of portfolio optimization, business intelligence, and automated water management in order to demonstrate how this abstraction can be applied to three different types of problems. For the automated water management platform a full solution to the problem is developed using this abstraction. This yields an automated decision process which decides how much water to release from the Piute Reservoir into the Sevier River during an irrigation season. Another motivation for developing these learning platforms is that they can be used to introduce students of all disciplines to automated decision making.