Rainfall prediction using machine learning

Rainfall prediction using machine learning

Rainfall prediction is a crucial aspect of weather forecasting and plays a significant role in various fields, including agriculture, water resource management, and disaster preparedness. In this chapter, the authors explore the application of two machine learning algorithms, random forest and cat b...

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Bibliographic Details
Published in:Advancements in Climate and Smart Environment Technology
Main Author: Kumar A.V.S.; Roshan S.A.; Dutta A.; Ray S.; Masadeh S.R.; Lakshmi G.P.; Michalopoulos D.; Nyayapati R.; Musirin I.B.; Kaur G.
Format: Book chapter
Language:English
Published: IGI Global 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198466583&doi=10.4018%2f979-8-3693-3807-0.ch009&partnerID=40&md5=06f83e6b006c6a7e9b09d9dc7be7a3f2
id 2-s2.0-85198466583
spelling 2-s2.0-85198466583
Kumar A.V.S.; Roshan S.A.; Dutta A.; Ray S.; Masadeh S.R.; Lakshmi G.P.; Michalopoulos D.; Nyayapati R.; Musirin I.B.; Kaur G.
Rainfall prediction using machine learning
2024
Advancements in Climate and Smart Environment Technology


10.4018/979-8-3693-3807-0.ch009
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198466583&doi=10.4018%2f979-8-3693-3807-0.ch009&partnerID=40&md5=06f83e6b006c6a7e9b09d9dc7be7a3f2
Rainfall prediction is a crucial aspect of weather forecasting and plays a significant role in various fields, including agriculture, water resource management, and disaster preparedness. In this chapter, the authors explore the application of two machine learning algorithms, random forest and cat boost, for predicting rainfall events. They utilize historical weather data from a specific location to train and evaluate the performance of both models. The evaluation metrics employed include accuracy, precision, recall, and F1-score. The findings suggest that incorporating additional features, such as humidity, can enhance the predictive capabilities of both random forest and cat boost. Overall, this project demonstrates the effectiveness of random forest and cat boost in predicting rainfall events. © 2024 by IGI Global. All rights reserved.
IGI Global

English
Book chapter
author Kumar A.V.S.; Roshan S.A.; Dutta A.; Ray S.; Masadeh S.R.; Lakshmi G.P.; Michalopoulos D.; Nyayapati R.; Musirin I.B.; Kaur G.
spellingShingle Kumar A.V.S.; Roshan S.A.; Dutta A.; Ray S.; Masadeh S.R.; Lakshmi G.P.; Michalopoulos D.; Nyayapati R.; Musirin I.B.; Kaur G.
Rainfall prediction using machine learning
author_facet Kumar A.V.S.; Roshan S.A.; Dutta A.; Ray S.; Masadeh S.R.; Lakshmi G.P.; Michalopoulos D.; Nyayapati R.; Musirin I.B.; Kaur G.
author_sort Kumar A.V.S.; Roshan S.A.; Dutta A.; Ray S.; Masadeh S.R.; Lakshmi G.P.; Michalopoulos D.; Nyayapati R.; Musirin I.B.; Kaur G.
title Rainfall prediction using machine learning
title_short Rainfall prediction using machine learning
title_full Rainfall prediction using machine learning
title_fullStr Rainfall prediction using machine learning
title_full_unstemmed Rainfall prediction using machine learning
title_sort Rainfall prediction using machine learning
publishDate 2024
container_title Advancements in Climate and Smart Environment Technology
container_volume
container_issue
doi_str_mv 10.4018/979-8-3693-3807-0.ch009
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198466583&doi=10.4018%2f979-8-3693-3807-0.ch009&partnerID=40&md5=06f83e6b006c6a7e9b09d9dc7be7a3f2
description Rainfall prediction is a crucial aspect of weather forecasting and plays a significant role in various fields, including agriculture, water resource management, and disaster preparedness. In this chapter, the authors explore the application of two machine learning algorithms, random forest and cat boost, for predicting rainfall events. They utilize historical weather data from a specific location to train and evaluate the performance of both models. The evaluation metrics employed include accuracy, precision, recall, and F1-score. The findings suggest that incorporating additional features, such as humidity, can enhance the predictive capabilities of both random forest and cat boost. Overall, this project demonstrates the effectiveness of random forest and cat boost in predicting rainfall events. © 2024 by IGI Global. All rights reserved.
publisher IGI Global
issn
language English
format Book chapter
accesstype
record_format scopus
collection Scopus
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