Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest

Dan Wang; Tao Hai; Doyinsola Ayandiran; Chijioke Victor Uzochukwu; Xiaofeng Ding; Celestine Iwendi; Zakaria Boulouard

doi:10.1007/978-3-031-37164-6_25

Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest

Dan Wang
, Tao Hai
, Doyinsola Ayandiran
, Chijioke Victor Uzochukwu
, Xiaofeng Ding
, Celestine Iwendi
, Zakaria Boulouard

Qiannan Normal College for Nationalities
Nanchang Institute of Science and Technology
University of Bolton
University of Hassan II Casablanca

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets.

Original language	English
Title of host publication	Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions
Editors	Celestine Iwendi, Zakaria Boulouard, Natalia Kryvinska
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	343-350
Number of pages	8
ISBN (Print)	9783031371639
DOIs	https://doi.org/10.1007/978-3-031-37164-6_25
State	Published - 2023
Externally published	Yes
Event	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 - Bolton, United Kingdom Duration: 24 Feb 2023 → 25 Feb 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	735 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Country/Territory	United Kingdom
City	Bolton
Period	24/02/23 → 25/02/23

Keywords

Classification
Decision tree
Rainfall prediction
Random forest

Access to Document

10.1007/978-3-031-37164-6_25

Cite this

Wang, D., Hai, T., Ayandiran, D., Uzochukwu, C. V., Ding, X., Iwendi, C., & Boulouard, Z. (2023). Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest. In C. Iwendi, Z. Boulouard, & N. Kryvinska (Eds.), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions (pp. 343-350). (Lecture Notes in Networks and Systems; Vol. 735 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37164-6_25

Wang, Dan ; Hai, Tao ; Ayandiran, Doyinsola et al. / Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest. Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. editor / Celestine Iwendi ; Zakaria Boulouard ; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. pp. 343-350 (Lecture Notes in Networks and Systems).

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title = "Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest",

abstract = "Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets.",

keywords = "Classification, Decision tree, Rainfall prediction, Random forest",

author = "Dan Wang and Tao Hai and Doyinsola Ayandiran and Uzochukwu, \{Chijioke Victor\} and Xiaofeng Ding and Celestine Iwendi and Zakaria Boulouard",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023 ; Conference date: 24-02-2023 Through 25-02-2023",

year = "2023",

doi = "10.1007/978-3-031-37164-6\_25",

language = "English",

isbn = "9783031371639",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "343--350",

editor = "Celestine Iwendi and Zakaria Boulouard and Natalia Kryvinska",

booktitle = "Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions",

address = "Germany",

}

Wang, D, Hai, T, Ayandiran, D, Uzochukwu, CV, Ding, X, Iwendi, C & Boulouard, Z 2023, Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest. in C Iwendi, Z Boulouard & N Kryvinska (eds), Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Lecture Notes in Networks and Systems, vol. 735 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 343-350, International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023, Bolton, United Kingdom, 24/02/23. https://doi.org/10.1007/978-3-031-37164-6_25

Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest. / Wang, Dan; Hai, Tao; Ayandiran, Doyinsola et al.
Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. ed. / Celestine Iwendi; Zakaria Boulouard; Natalia Kryvinska. Springer Science and Business Media Deutschland GmbH, 2023. p. 343-350 (Lecture Notes in Networks and Systems; Vol. 735 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest

AU - Wang, Dan

AU - Hai, Tao

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AU - Uzochukwu, Chijioke Victor

AU - Ding, Xiaofeng

AU - Iwendi, Celestine

AU - Boulouard, Zakaria

PY - 2023

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N2 - Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets.

AB - Climate change has made accurate rainfall forecasting more difficult than ever. In this paper, the decision tree algorithm and Random Forest is used to predict the rainfall accuracy based on historical climate data. The classification and regression tree (CART) approach is employed to this result, producing a better accuracy rate. The algorithm can determine the probabilities of rain on any given day, making it an ideal choice for various applications involving large datasets.

KW - Classification

KW - Decision tree

KW - Rainfall prediction

KW - Random forest

UR - https://www.scopus.com/pages/publications/85174550520

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DO - 10.1007/978-3-031-37164-6_25

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AN - SCOPUS:85174550520

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BT - Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions

A2 - Iwendi, Celestine

A2 - Boulouard, Zakaria

A2 - Kryvinska, Natalia

PB - Springer Science and Business Media Deutschland GmbH

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Y2 - 24 February 2023 through 25 February 2023

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Wang D, Hai T, Ayandiran D, Uzochukwu CV, Ding X, Iwendi C et al. Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest. In Iwendi C, Boulouard Z, Kryvinska N, editors, Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions. Springer Science and Business Media Deutschland GmbH. 2023. p. 343-350. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-37164-6_25

Accuracy Prediction of Rainfall Using Decision Tree Algorithm and Random Forest

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Keywords

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