Due to the negative impact on the environment of conventional electric power generation methods, especially coal and oil-fired generating plants, wind power as an alternative for sustainable energy has received more attention in recent years. The purpose of this project was to apply Geographic Information System (GIS), integrated with Multi Criteria Decision Making (MCDM), for identifying suitable areas for wind turbine applications in Texas. Factors taken into consideration included socioeconomic criteria such as distance to highways, proximity to airports and urban areas, localized environmental criteria such as terrain slope and distance to rivers, affected waterbodies, and wildlife management areas. Also included is the most critical criterion, the wind power density defined by the National Renewable Energy Laboratory that integrated the abundance and quality of wind, the complexity of the terrain, and the geographical variability of the resources. GIS analysis models were built by applying different map overlay techniques, including Weighted Sum, Weighted Overlay and Fuzzy Overlay. For Weighted Sum and Weighted Overlay, each input factor was classified and weighted through an Analytical Hierarchy Process (AHP). The weights for each criterion were assigned using a pair-wise comparison, where the Wind Class received the greatest weight of 0.377 followed by slope with 0.2509. As to Fuzzy Overlay, different methods, including Large, Small, MSLarge, and MSSmall, were used to assign fuzzy membership on each participating criterion, followed by using the overlay methods of SUM, PRODUCT, AND, and OR. Each model output was rescaled to having a range of 1 to 5, where 5 represents a location that is highly suitable for windmill development. Each GIS model output was validated by existing wind turbine locations. The suitability index value for each existing wind turbine location was identified for each model output. The Fuzzy Overlay Three model resulted in the highest mean index value of 3.86, followed by the Weighted Overlay of 3.77, and the Weighted Sum of 3.71. It was found that the model outputs were statistically different in terms of accuracy. A general trend was observed that the western and northwestern portions of Texas are the most feasible areas for wind turbine installation.
","abstract_html":"<p>Due to the negative impact on the environment of conventional electric power generation methods, especially coal and oil-fired generating plants, wind power as an alternative for sustainable energy has received more attention in recent years. The purpose of this project was to apply Geographic Information System (GIS), integrated with Multi Criteria Decision Making (MCDM), for identifying suitable areas for wind turbine applications in Texas. Factors taken into consideration included socioeconomic criteria such as distance to highways, proximity to airports and urban areas, localized environmental criteria such as terrain slope and distance to rivers, affected waterbodies, and wildlife management areas. Also included is the most critical criterion, the wind power density defined by the National Renewable Energy Laboratory that integrated the abundance and quality of wind, the complexity of the terrain, and the geographical variability of the resources. GIS analysis models were built by applying different map overlay techniques, including Weighted Sum, Weighted Overlay and Fuzzy Overlay. For Weighted Sum and Weighted Overlay, each input factor was classified and weighted through an Analytical Hierarchy Process (AHP). The weights for each criterion were assigned using a pair-wise comparison, where the Wind Class received the greatest weight of 0.377 followed by slope with 0.2509. As to Fuzzy Overlay, different methods, including Large, Small, MSLarge, and MSSmall, were used to assign fuzzy membership on each participating criterion, followed by using the overlay methods of SUM, PRODUCT, AND, and OR. Each model output was rescaled to having a range of 1 to 5, where 5 represents a location that is highly suitable for windmill development. Each GIS model output was validated by existing wind turbine locations. The suitability index value for each existing wind turbine location was identified for each model output. The Fuzzy Overlay Three model resulted in the highest mean index value of 3.86, followed by the Weighted Overlay of 3.77, and the Weighted Sum of 3.71. It was found that the model outputs were statistically different in terms of accuracy. A general trend was observed that the western and northwestern portions of Texas are the most feasible areas for wind turbine installation.</p>","abstract_has_math":false,"creators":["Milecz, Robert Laszlo"],"institution":null,"degree_name":"Master of Science - Environmental Sciences","degree_level":"Thesis","degree_discipline":"Forestry","degree_department":null,"school":null,"contributors":["I-Kuai Hung","Kenneth Farrish","Daniel Unger"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-01T07:00:00Z","date_published":"2020-08-01T07:00:00Z","updated_at":"2026-07-24T04:30:30Z","subjects":["renewable energy","GIS","suitability analysis","wind power","Environmental Sciences","Geographic Information Sciences","Spatial Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.sfasu.edu/etds/339","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["I-Kuai Hung","Kenneth Farrish","Daniel Unger"]},{"key":"dc:creator","label":"Author","values":["Milecz, Robert Laszlo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-08-04T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Forestry"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science - Environmental Sciences"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["renewable energy","GIS","suitability analysis","wind power","Environmental Sciences","Geographic Information Sciences","Spatial Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.sfasu.edu/etds/339"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Due to the negative impact on the environment of conventional electric power generation methods, especially coal and oil-fired generating plants, wind power as an alternative for sustainable energy has received more attention in recent years. The purpose of this project was to apply Geographic Information System (GIS), integrated with Multi Criteria Decision Making (MCDM), for identifying suitable areas for wind turbine applications in Texas. Factors taken into consideration included socioeconomic criteria such as distance to highways, proximity to airports and urban areas, localized environmental criteria such as terrain slope and distance to rivers, affected waterbodies, and wildlife management areas. Also included is the most critical criterion, the wind power density defined by the National Renewable Energy Laboratory that integrated the abundance and quality of wind, the complexity of the terrain, and the geographical variability of the resources. GIS analysis models were built by applying different map overlay techniques, including Weighted Sum, Weighted Overlay and Fuzzy Overlay. For Weighted Sum and Weighted Overlay, each input factor was classified and weighted through an Analytical Hierarchy Process (AHP). The weights for each criterion were assigned using a pair-wise comparison, where the Wind Class received the greatest weight of 0.377 followed by slope with 0.2509. As to Fuzzy Overlay, different methods, including Large, Small, MSLarge, and MSSmall, were used to assign fuzzy membership on each participating criterion, followed by using the overlay methods of SUM, PRODUCT, AND, and OR. Each model output was rescaled to having a range of 1 to 5, where 5 represents a location that is highly suitable for windmill development. Each GIS model output was validated by existing wind turbine locations. The suitability index value for each existing wind turbine location was identified for each model output. The Fuzzy Overlay Three model resulted in the highest mean index value of 3.86, followed by the Weighted Overlay of 3.77, and the Weighted Sum of 3.71. It was found that the model outputs were statistically different in terms of accuracy. A general trend was observed that the western and northwestern portions of Texas are the most feasible areas for wind turbine installation.
"]},{"key":"dc:title","label":"Title","values":["LAND SUITABILITY ANALYSIS FOR WINDFARM DEVELOPMENT IN TEXAS"]}]}],"canonical_facts":{"dc:contributor":["I-Kuai Hung","Kenneth Farrish","Daniel Unger"],"dc:creator":["Milecz, Robert Laszlo"],"dc:date.available":["2020-08-04T07:00:00Z"],"dc:description.abstract":["Due to the negative impact on the environment of conventional electric power generation methods, especially coal and oil-fired generating plants, wind power as an alternative for sustainable energy has received more attention in recent years. The purpose of this project was to apply Geographic Information System (GIS), integrated with Multi Criteria Decision Making (MCDM), for identifying suitable areas for wind turbine applications in Texas. Factors taken into consideration included socioeconomic criteria such as distance to highways, proximity to airports and urban areas, localized environmental criteria such as terrain slope and distance to rivers, affected waterbodies, and wildlife management areas. Also included is the most critical criterion, the wind power density defined by the National Renewable Energy Laboratory that integrated the abundance and quality of wind, the complexity of the terrain, and the geographical variability of the resources. GIS analysis models were built by applying different map overlay techniques, including Weighted Sum, Weighted Overlay and Fuzzy Overlay. For Weighted Sum and Weighted Overlay, each input factor was classified and weighted through an Analytical Hierarchy Process (AHP). The weights for each criterion were assigned using a pair-wise comparison, where the Wind Class received the greatest weight of 0.377 followed by slope with 0.2509. As to Fuzzy Overlay, different methods, including Large, Small, MSLarge, and MSSmall, were used to assign fuzzy membership on each participating criterion, followed by using the overlay methods of SUM, PRODUCT, AND, and OR. Each model output was rescaled to having a range of 1 to 5, where 5 represents a location that is highly suitable for windmill development. Each GIS model output was validated by existing wind turbine locations. The suitability index value for each existing wind turbine location was identified for each model output. The Fuzzy Overlay Three model resulted in the highest mean index value of 3.86, followed by the Weighted Overlay of 3.77, and the Weighted Sum of 3.71. It was found that the model outputs were statistically different in terms of accuracy. A general trend was observed that the western and northwestern portions of Texas are the most feasible areas for wind turbine installation.
"],"dc:identifier":["https://scholarworks.sfasu.edu/etds/339"],"dc:subject":["renewable energy","GIS","suitability analysis","wind power","Environmental Sciences","Geographic Information Sciences","Spatial Science"],"dc:title":["LAND SUITABILITY ANALYSIS FOR WINDFARM DEVELOPMENT IN TEXAS"],"thesis:degree_discipline":["Forestry"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science - Environmental Sciences"]},"updated_at":"2026-07-24T04:30:30Z"}