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INDUCTIVE AND MODEL-TREE-BASED APPROACHES FOR FORECASTING TEMPERATURE
- Date Issued:
- 2021
- Abstract/Description:
- Inductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface temperatures (SSTs), correction factors derived using historical data. A global model using data from all the sites is also developed. The performances of the models were evaluated using observed temperature records and several error and performance measures. A composite measure combining multiple error and performance measures is developed to select the best model. MT approach-based and regression models with SSTs and correction factors along with lagged temperature values are found to be best models for forecasting temperature based on assessments of composite measures and error diagnostics.
Title: | INDUCTIVE AND MODEL-TREE-BASED APPROACHES FOR FORECASTING TEMPERATURE. |
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Name(s): |
Schauer, Alexis , author Teegavarapu, Ramesh S. V. , Thesis advisor Florida Atlantic University, Degree grantor Department of Civil, Environmental and Geomatics Engineering College of Engineering and Computer Science |
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Type of Resource: | text | |
Genre: | Electronic Thesis Or Dissertation | |
Date Created: | 2021 | |
Date Issued: | 2021 | |
Publisher: | Florida Atlantic University | |
Place of Publication: | Boca Raton, Fla. | |
Physical Form: | application/pdf | |
Extent: | 222 p. | |
Language(s): | English | |
Abstract/Description: | Inductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface temperatures (SSTs), correction factors derived using historical data. A global model using data from all the sites is also developed. The performances of the models were evaluated using observed temperature records and several error and performance measures. A composite measure combining multiple error and performance measures is developed to select the best model. MT approach-based and regression models with SSTs and correction factors along with lagged temperature values are found to be best models for forecasting temperature based on assessments of composite measures and error diagnostics. | |
Identifier: | FA00013856 (IID) | |
Degree granted: | Thesis (MS)--Florida Atlantic University, 2021. | |
Collection: | FAU Electronic Theses and Dissertations Collection | |
Note(s): | Includes bibliography. | |
Subject(s): |
Temperature Forecasting--Mathematical models |
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Persistent Link to This Record: | http://purl.flvc.org/fau/fd/FA00013856 | |
Use and Reproduction: | Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder. | |
Use and Reproduction: | http://rightsstatements.org/vocab/InC/1.0/ | |
Host Institution: | FAU | |
Is Part of Series: | Florida Atlantic University Digital Library Collections. |