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Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satelli...

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Main Authors:	Latif S.D., Alyaa Binti Hazrin N., Hoon Koo C., Lin Ng J., Chaplot B., Feng Huang Y., El-Shafie A., Najah Ahmed A.
Other Authors:	57216081524
Format:	Review
Published:	Elsevier B.V. 2024
Subjects:	Hybrid models Machine learning Prediction Rainfall Remote sensing Climate models Forecasting Learning algorithms Learning systems Long short-term memory Rain Regression analysis Satellite imagery Hybrid model Learn+ Machine-learning Model learning Prediction modelling Predictive models Rainfall prediction Remote sensing approaches Remote-sensing Statistic modeling
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id	my.uniten.dspace-34018
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spelling	my.uniten.dspace-340182024-10-14T11:17:40Z Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches Latif S.D. Alyaa Binti Hazrin N. Hoon Koo C. Lin Ng J. Chaplot B. Feng Huang Y. El-Shafie A. Najah Ahmed A. 57216081524 58642255900 58641117700 57192698412 57201316781 55807263900 16068189400 58136810800 Hybrid models Machine learning Prediction Rainfall Remote sensing Climate models Forecasting Learning algorithms Learning systems Long short-term memory Rain Regression analysis Satellite imagery Hybrid model Learn+ Machine-learning Model learning Prediction modelling Predictive models Rainfall prediction Remote sensing approaches Remote-sensing Statistic modeling Remote sensing Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satellite imagery, radar data, and ground-based observations are used and using aircraft or satellites, and remote sensing (RS) collects data on distant objects or locations. Satellites and radar are used to gather regional precipitation data for hybrid models. An algorithm trained on historical rainfall measurements would then process the data. Using remote monitoring instrument input features, the machine-learning model can predict precipitation. Evaluation of machine learning regression methods is based on the degree of agreement between predicted and observed values. The RMSE, R2, and MAE statistical measures check on the precision of a prediction or forecasting model. Machine learning excels at rainfall prediction regardless of climate or timescale. As one of the more popular models for predicting rainfall, the LSTM models demonstrate their superiority. Remote sensing and hybrid predictive models should be investigated further due to their scarcity. � 2023 THE AUTHORS Final 2024-10-14T03:17:40Z 2024-10-14T03:17:40Z 2023 Review 10.1016/j.aej.2023.09.060 2-s2.0-85173857230 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173857230&doi=10.1016%2fj.aej.2023.09.060&partnerID=40&md5=cd370191babf611b33890ad186710d8c https://irepository.uniten.edu.my/handle/123456789/34018 82 16 25 All Open Access Gold Open Access Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Hybrid models Machine learning Prediction Rainfall Remote sensing Climate models Forecasting Learning algorithms Learning systems Long short-term memory Rain Regression analysis Satellite imagery Hybrid model Learn+ Machine-learning Model learning Prediction modelling Predictive models Rainfall prediction Remote sensing approaches Remote-sensing Statistic modeling Remote sensing
spellingShingle	Hybrid models Machine learning Prediction Rainfall Remote sensing Climate models Forecasting Learning algorithms Learning systems Long short-term memory Rain Regression analysis Satellite imagery Hybrid model Learn+ Machine-learning Model learning Prediction modelling Predictive models Rainfall prediction Remote sensing approaches Remote-sensing Statistic modeling Remote sensing Latif S.D. Alyaa Binti Hazrin N. Hoon Koo C. Lin Ng J. Chaplot B. Feng Huang Y. El-Shafie A. Najah Ahmed A. Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
description	Using a comparison of three different major types, the best predictive model was determined. Statistical models and machine learning algorithms automatically learn and improve based on data. Deep learning uses neural networks to learn complex data patterns and relationships. A combination of satellite imagery, radar data, and ground-based observations are used and using aircraft or satellites, and remote sensing (RS) collects data on distant objects or locations. Satellites and radar are used to gather regional precipitation data for hybrid models. An algorithm trained on historical rainfall measurements would then process the data. Using remote monitoring instrument input features, the machine-learning model can predict precipitation. Evaluation of machine learning regression methods is based on the degree of agreement between predicted and observed values. The RMSE, R2, and MAE statistical measures check on the precision of a prediction or forecasting model. Machine learning excels at rainfall prediction regardless of climate or timescale. As one of the more popular models for predicting rainfall, the LSTM models demonstrate their superiority. Remote sensing and hybrid predictive models should be investigated further due to their scarcity. � 2023 THE AUTHORS
author2	57216081524
author_facet	57216081524 Latif S.D. Alyaa Binti Hazrin N. Hoon Koo C. Lin Ng J. Chaplot B. Feng Huang Y. El-Shafie A. Najah Ahmed A.
format	Review
author	Latif S.D. Alyaa Binti Hazrin N. Hoon Koo C. Lin Ng J. Chaplot B. Feng Huang Y. El-Shafie A. Najah Ahmed A.
author_sort	Latif S.D.
title	Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
title_short	Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
title_full	Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
title_fullStr	Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
title_full_unstemmed	Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches
title_sort	assessing rainfall prediction models: exploring the advantages of machine learning and remote sensing approaches
publisher	Elsevier B.V.
publishDate	2024
_version_	1814061162854088704
score	13.211869

Assessing rainfall prediction models: Exploring the advantages of machine learning and remote sensing approaches

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