Date of Award:

12-2011

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Huifang Dou

Committee

Huifang Dou

Committee

YangQuan Chen

Committee

Jacob Gunther

Abstract

The Center for Self-Organizing Intelligent Systems (CSOIS) has been successful in developing personal remote sensing systems. These systems can be further enhanced to monitor important resources of earth such as soil moisture content. Soil moisture is the major component of the soil in relation to plant growth. Soil water dissolves salts and makes up the soil solution, which is important as medium for supply of nutrients to growing plants.

Usually, measuring such physical quantities would include using derived formulas that mathematically describe the relationships between the parameters involved. With the advancements in computer modeling, other methods such as empirical modeling have evolved which can be used to develop models for measurement of physical quantities such as soil moisture content. In this kind of modeling, empirical data are used to pick a right model, and to calibrate and test it. The relevant data constitutes measured values of predetermined input parameters and the output parameter which is being modeled. The input parameters can be chosen by experience emphasizing the fact that there should be a minimum correlation between them. The important step in such a kind of model development is to choose the techniques to be used in finding an appropriate model. The main objective of the present work lies in understanding the mathematical backgrounds of different advanced techniques used in empirical model development for measurement of soil moisture content, analyzing the results, and working in the direction of improving those contemporary methods.

Checksum

7bf3b66e676d45967e423c72e217d76d

Comments

Publication made available electronically December 21, 2011.

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