Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods

Temperature is a key variable for understanding climate variability and change. However, climatological summary statistics based on daily minimum and maximum temperatures contain inhomogeneities that can largely affect estimations done at a local scale. Thus, this study aimed to use a spatiotemporal...

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Main Authors: Gammad, Alyanna Marrielle Cabang, Mejia, Jo-Anne April Penullar
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Language:English
Published: Animo Repository 2023
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Online Access:https://animorepository.dlsu.edu.ph/etdb_math/27
https://animorepository.dlsu.edu.ph/context/etdb_math/article/1028/viewcontent/2023_Gammad_Mejia_Prediction_of_Daily_Minimum_and_Maximum_Temperature_Full_text.pdf
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Institution: De La Salle University
Language: English
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spelling oai:animorepository.dlsu.edu.ph:etdb_math-10282023-09-20T00:42:16Z Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods Gammad, Alyanna Marrielle Cabang Mejia, Jo-Anne April Penullar Temperature is a key variable for understanding climate variability and change. However, climatological summary statistics based on daily minimum and maximum temperatures contain inhomogeneities that can largely affect estimations done at a local scale. Thus, this study aimed to use a spatiotemporal Gaussian process model (STGPM) to impute and predict three-hourly measurements from neighboring stations in the Greater Manila Area that may be used to obtain summaries of temperature extremes. Due to the computationally expensive nature of this method, temperature extrema predictions using Artificial Neural Network (ANN) methods such as feed-forward back propagation (FFBP), radial basis function network (RBFN), and generalized regression neural network (GRNN) have been obtained as an alternative and for prediction performance comparison. Here, comparisons are made using R2, MSE, RMSE, and IA. As the ANN models are limited to the prediction of temperature extrema, the STGPM inherently gives more information through the imputation and prediction of three-hourly temperature. When comparing performance scores, The STGPM also performs best based on the measures of model performance namely MSE, RMSE, R2, and IA. However, among the ANN models, GRNN performs best in terms of the following measures of model performance: R2, MSE, and RMSE. 2023-01-01T08:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etdb_math/27 https://animorepository.dlsu.edu.ph/context/etdb_math/article/1028/viewcontent/2023_Gammad_Mejia_Prediction_of_Daily_Minimum_and_Maximum_Temperature_Full_text.pdf Mathematics and Statistics Bachelor's Theses English Animo Repository Temperature forecasting, Minimum Statistics and Probability
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Temperature forecasting, Minimum
Statistics and Probability
spellingShingle Temperature forecasting, Minimum
Statistics and Probability
Gammad, Alyanna Marrielle Cabang
Mejia, Jo-Anne April Penullar
Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods
description Temperature is a key variable for understanding climate variability and change. However, climatological summary statistics based on daily minimum and maximum temperatures contain inhomogeneities that can largely affect estimations done at a local scale. Thus, this study aimed to use a spatiotemporal Gaussian process model (STGPM) to impute and predict three-hourly measurements from neighboring stations in the Greater Manila Area that may be used to obtain summaries of temperature extremes. Due to the computationally expensive nature of this method, temperature extrema predictions using Artificial Neural Network (ANN) methods such as feed-forward back propagation (FFBP), radial basis function network (RBFN), and generalized regression neural network (GRNN) have been obtained as an alternative and for prediction performance comparison. Here, comparisons are made using R2, MSE, RMSE, and IA. As the ANN models are limited to the prediction of temperature extrema, the STGPM inherently gives more information through the imputation and prediction of three-hourly temperature. When comparing performance scores, The STGPM also performs best based on the measures of model performance namely MSE, RMSE, R2, and IA. However, among the ANN models, GRNN performs best in terms of the following measures of model performance: R2, MSE, and RMSE.
format text
author Gammad, Alyanna Marrielle Cabang
Mejia, Jo-Anne April Penullar
author_facet Gammad, Alyanna Marrielle Cabang
Mejia, Jo-Anne April Penullar
author_sort Gammad, Alyanna Marrielle Cabang
title Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods
title_short Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods
title_full Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods
title_fullStr Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods
title_full_unstemmed Prediction of daily minimum and maximum temperature measurements in the Greater Manila Area using gaussian process modeling for bias correction and artificial neural network methods
title_sort prediction of daily minimum and maximum temperature measurements in the greater manila area using gaussian process modeling for bias correction and artificial neural network methods
publisher Animo Repository
publishDate 2023
url https://animorepository.dlsu.edu.ph/etdb_math/27
https://animorepository.dlsu.edu.ph/context/etdb_math/article/1028/viewcontent/2023_Gammad_Mejia_Prediction_of_Daily_Minimum_and_Maximum_Temperature_Full_text.pdf
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