Accuracy prediction of rainfall using Decision Tree algorithm and Random Forest

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

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

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Accuracy prediction of rainfall using Decision Tree algorithm and Random Forest

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Accuracy prediction of rainfall using Decision Tree algorithm and Random Forest

Dan Wang, Tao Hai, Doyinsola Ayandiran, Chijioke Victor Uzochukwu, Xiaofeng Ding, Celestine Iwendi and Z. Boulouard

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, 735

ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (Bolton, United Kingdom, 24/02/2023–25/02/2023)

24/09/2023

DOI: https://doi.org/10.1007/978-3-031-37164-6_25

Abstract

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

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Title: Accuracy prediction of rainfall using Decision Tree algorithm and Random Forest
Creators - without role: Dan Wang - Qiannan Normal College For Nationalities
Tao Hai
Doyinsola Ayandiran - University of Greater Manchester
Chijioke Victor Uzochukwu - University of Greater Manchester
Xiaofeng Ding
Celestine Iwendi - University of Greater Manchester
Z. Boulouard - University of Hassan II Casablanca
Contributors: Celestine Iwendi (Editor) - University of Greater Manchester
Z. Boulouard (Editor) - University of Hassan II Casablanca
N. Kryvinska (Editor)
Publication Details: 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
Conference: ICACTCE23 - International Conference on Advances in Communication Technology and Computer Engineering (Bolton, United Kingdom, 24/02/2023–25/02/2023)
Series: Lecture Notes in Networks and Systems; 735
Publisher: Springer
Format: text
Identifiers: 999816708841; 2367-3370; 2367-3389; 9783031371639; 2367-3389
Academic Unit: Computing; Games
Language: English
Resource Type: Conference proceeding
Comment: Paper given at published in the Proceedings of ICACTCE23 24-25th February
Generative AI Statement: https://icactce-conf.com/

Accuracy prediction of rainfall using Decision Tree algorithm and Random Forest

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