{"id":{"repo_id":"uno","oai_identifier":"oai:scholarworks.uno.edu:td-2078"},"canonical_url":"https://search.dev.ndltd.org/etd/uno/oai:scholarworks.uno.edu:td-2078","repository":{"repo_id":"uno","name":"University of New Orleans","base_url":"https://scholarworks.uno.edu/do/oai/"},"display":{"title":"Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery","abstract":"Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed.","abstract_html":"Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed.","abstract_has_math":false,"creators":["Paduru, Anirudh"],"institution":null,"degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Charalampidis, Dimitrios","Jilkov, Vesselin","Chen, Huimin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-12-20T08:00:00Z","date_published":"2009-12-20T08:00:00Z","updated_at":"2026-07-24T05:29:02Z","subjects":["Radial Basis Function Neural Network","Forecasting","Nowcasting","Tracking","Rain Events","Non-Rain Events","Gaussian Functions","Mahalanobis Distance","Pyramidal Synthesis","Cascade Synthesis","Coarse Resolution","3D Elevations"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarworks.uno.edu/td/1097","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Charalampidis, Dimitrios","Jilkov, Vesselin","Chen, Huimin"]},{"key":"dc:creator","label":"Author","values":["Paduru, Anirudh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Radial Basis Function Neural Network","Forecasting","Nowcasting","Tracking","Rain Events","Non-Rain Events","Gaussian Functions","Mahalanobis Distance","Pyramidal Synthesis","Cascade Synthesis","Coarse Resolution","3D Elevations"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarworks.uno.edu/td/1097"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed."]},{"key":"dc:title","label":"Title","values":["Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery"]}]}],"canonical_facts":{"dc:contributor":["Charalampidis, Dimitrios","Jilkov, Vesselin","Chen, Huimin"],"dc:creator":["Paduru, Anirudh"],"dc:description.abstract":["Weather radar imagery is important for several remote sensing applications including tracking of storm fronts and radar echo classification. In particular, tracking of precipitation events is useful for both forecasting and classification of rain/non-rain events since non-rain events usually appear to be static compared to rain events. Recent weather radar imaging-based forecasting approaches [3] consider that precipitation events can be modeled as a combination of localized functions using Radial Basis Function Neural Networks (RBFNNs). Tracking of rain events can be performed by tracking the parameters of these localized functions. The RBFNN-based techniques used in forecasting are not only computationally expensive, but also moderately effective in modeling small size precipitation events. In this thesis, an existing RBFNN technique [3] was implemented to verify its computational efficiency and forecasting effectiveness. The feasibility of modeling precipitation events using RBFNN effectively was evaluated, and several modifications to the existing technique have been proposed."],"dc:identifier":["https://scholarworks.uno.edu/td/1097"],"dc:subject":["Radial Basis Function Neural Network","Forecasting","Nowcasting","Tracking","Rain Events","Non-Rain Events","Gaussian Functions","Mahalanobis Distance","Pyramidal Synthesis","Cascade Synthesis","Coarse Resolution","3D Elevations"],"dc:title":["Fast Algorithm for Modeling of Rain Events in Weather Radar Imagery"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."]},"updated_at":"2026-07-24T05:29:02Z"}