With the growing interest in the Marcellus Shale and its natural gas deposits, there are opportunities to purchase and hold land for investment purposes. A robust decision tool is needed to help guide investors towards the most profitable properties. Artificial neural networks have many unique benefits that make them an ideal candidate for this purpose.
The artificial neural networks created in this study had nine independent variables. Combinations of these nine variables were created to describe 300 theoretical properties available for purchase. Each of these properties were then evaluated by an expert in the field and given a score from one to five to rate its investment potential, which was the dependent variable.
Sixteen different network architectures were used to create over 200 neural networks. However, none of these networks met the criteria established to determine success. This is likely due to the unreliability in the data used to train the network, evidenced by the expert’s inability to reproduce previously assigned scores.
","abstract_html":"<p>With the growing interest in the Marcellus Shale and its natural gas deposits, there are opportunities to purchase and hold land for investment purposes. A robust decision tool is needed to help guide investors towards the most profitable properties. Artificial neural networks have many unique benefits that make them an ideal candidate for this purpose.</p> <p>The artificial neural networks created in this study had nine independent variables. Combinations of these nine variables were created to describe 300 theoretical properties available for purchase. Each of these properties were then evaluated by an expert in the field and given a score from one to five to rate its investment potential, which was the dependent variable.</p> <p>Sixteen different network architectures were used to create over 200 neural networks. However, none of these networks met the criteria established to determine success. This is likely due to the unreliability in the data used to train the network, evidenced by the expert’s inability to reproduce previously assigned scores.</p>","abstract_has_math":false,"creators":["Denecour, Micah D."],"institution":null,"degree_name":"MS in Industrial Engineering","degree_level":null,"degree_discipline":"Industrial and Manufacturing Engineering","degree_department":null,"school":null,"contributors":["Lizabeth Schlemer"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-03-01T08:00:00Z","date_published":"2011-03-01T08:00:00Z","updated_at":"2026-07-24T01:31:11Z","subjects":["Operational Research"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2011.41"],"render_values":[{"text":"10.15368/theses.2011.41","href":"https://doi.org/10.15368/theses.2011.41","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/487","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lizabeth Schlemer"]},{"key":"dc:creator","label":"Author","values":["Denecour, Micah D."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2011-03-27T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Manufacturing Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Industrial Engineering"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Operational Research"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/487","10.15368/theses.2011.41"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["With the growing interest in the Marcellus Shale and its natural gas deposits, there are opportunities to purchase and hold land for investment purposes. A robust decision tool is needed to help guide investors towards the most profitable properties. Artificial neural networks have many unique benefits that make them an ideal candidate for this purpose.
The artificial neural networks created in this study had nine independent variables. Combinations of these nine variables were created to describe 300 theoretical properties available for purchase. Each of these properties were then evaluated by an expert in the field and given a score from one to five to rate its investment potential, which was the dependent variable.
Sixteen different network architectures were used to create over 200 neural networks. However, none of these networks met the criteria established to determine success. This is likely due to the unreliability in the data used to train the network, evidenced by the expert’s inability to reproduce previously assigned scores.
"]},{"key":"dc:title","label":"Title","values":["Artificial Neural Networking as a Decision Tool for Natural Gas Investment"]}]}],"canonical_facts":{"dc:contributor":["Lizabeth Schlemer"],"dc:creator":["Denecour, Micah D."],"dc:date.available":["2011-03-27T07:00:00Z"],"dc:description.abstract":["With the growing interest in the Marcellus Shale and its natural gas deposits, there are opportunities to purchase and hold land for investment purposes. A robust decision tool is needed to help guide investors towards the most profitable properties. Artificial neural networks have many unique benefits that make them an ideal candidate for this purpose.
The artificial neural networks created in this study had nine independent variables. Combinations of these nine variables were created to describe 300 theoretical properties available for purchase. Each of these properties were then evaluated by an expert in the field and given a score from one to five to rate its investment potential, which was the dependent variable.
Sixteen different network architectures were used to create over 200 neural networks. However, none of these networks met the criteria established to determine success. This is likely due to the unreliability in the data used to train the network, evidenced by the expert’s inability to reproduce previously assigned scores.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/487","10.15368/theses.2011.41"],"dc:subject":["Operational Research"],"dc:title":["Artificial Neural Networking as a Decision Tool for Natural Gas Investment"],"thesis:degree_discipline":["Industrial and Manufacturing Engineering"],"thesis:degree_name":["MS in Industrial Engineering"]},"updated_at":"2026-07-24T01:31:11Z"}