Skip to main content
EXTENDED SEARCH
Log in
Top
Published in:
Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Uncertainty Quantification for Climate Precipitation Prediction by Decision Tree

Authors : Vinicius S. Monego, Juliana A. Anochi, Haroldo F. de Campos Velho

Published in: Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This chapter delves into the application of the LightGBM decision tree algorithm for climate precipitation prediction and uncertainty quantification. It compares the performance of LightGBM with traditional differential equation-based methods, showing that LightGBM can provide more accurate predictions and better estimate uncertainty. The study focuses on South America, where precipitation is crucial for energy planning and disaster management. The chapter also discusses the computational efficiency of LightGBM, highlighting its potential to improve forecasting with minimal resource requirements. The results indicate that LightGBM can capture intense precipitation patterns and accurately predict uncertainty, making it a promising tool for climate prediction and planning.
AI Generated

This summary of the content was generated with the help of AI.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Business + Economics & Engineering + Technology"

Online-Abonnement

Springer Professional "Business + Economics & Engineering + Technology" gives you access to:

  • more than 102.000 books
  • more than 537 journals

from the following subject areas:

  • Automotive
  • Construction + Real Estate
  • Business IT + Informatics
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Mechanical Engineering + Materials
  • Insurance + Risk


Secure your knowledge advantage now!

inform now

Springer Professional "Engineering + Technology"

Online-Abonnement

Springer Professional "Engineering + Technology" gives you access to:

  • more than 67.000 books
  • more than 390 journals

from the following specialised fileds:

  • Automotive
  • Business IT + Informatics
  • Construction + Real Estate
  • Electrical Engineering + Electronics
  • Energy + Sustainability
  • Mechanical Engineering + Materials





 

Secure your knowledge advantage now!

inform now

Springer Professional "Business + Economics"

Online-Abonnement

Springer Professional "Business + Economics" gives you access to:

  • more than 67.000 books
  • more than 340 journals

from the following specialised fileds:

  • Construction + Real Estate
  • Business IT + Informatics
  • Finance + Banking
  • Management + Leadership
  • Marketing + Sales
  • Insurance + Risk



Secure your knowledge advantage now!

inform now
previous chapter Stochastic Kriging-Based Optimization Applied in Direct Policy Search for Decision Problems in Infrastructure Planning
next chapter Road Accidents Forecasting: An Uncertainty Quantification Model for Pre-disaster Management in Moroccan Context
Footnotes
Literature
1.
2.
3.
Anwar, M.T., Winarno, E., Hadikurniawati, W., Novita, M.: Rainfall prediction using extreme gradient boosting. In: 2nd Annual Conference of Science and Technology (ANCOSET 2020), 28 November, Malang, Indonesia (2020)
4.
Bauer, P., Thorpe, A., Brunet, G.: The quiet revolution of numerical weather prediction. Nature 4(525), 47–55 (2015)CrossRef
5.
Ferraz, S.E.T., Souto, R.P., Dias, P.L.S., Campos Velho, H.F., Ruivo, H.M.: Analysis for precipitation climate prediction on South of Brazil, pp. 500–596. Ciência e Natura, Special Issue (2013)
6.
Figueroa, S.N., et al.: The Brazilian global atmospheric model (BAM): performance for tropical rainfall forecasting and sensitivity to convective scheme and horizontal resolution. Weather Forecast. 31(5), 1547–1572 (2016)CrossRef
7.
Freitas, J.H.V., França, G.B., Menezes, W.F.: Deep convection forecasting using decision tree in Rio de Janeiro metropolitan area. Anuário do Instituto de Geociências (UFRJ. Brazil) 42(1), 127–134 (2019)
8.
Haltiner, G.J., Williams, R.T.: Numerical Prediction and Dynamic Meteorology. Wiley, Hoboken (1980)
9.
Kalnay, E.: Atmospheric Modeling. Data Assimilation and Predictability, Cambridge University Press, Cambridge (2002)
10.
11.
Lima, G.C., Toledo, A.L.L., Bourikas, L.: The role of national energy policies and life cycle emissions of PV systems in reducing global net emissions of greenhouse gases. Energies 14(4), 961 (2021)CrossRef
12.
Ma, L., Zhang, G., Lu, E.: Using the gradient boosting decision tree to improve the delineation of hourly rain areas during the summer from Aavanced Himawari imager data. J. Hydrometeorology 19(5), 761–776 (2018)
13.
Mendonça, A.K.S., Bornia, A.C.: Wind speed analysis based on the logarithmic wind shear model: a case study for some Brazilian cities. Res. Soc. Dev. 9(7), e298973984 (2020)CrossRef
14.
15.
Ukkonen, P., Makela, A.: Evaluation of machine learning classifiers for predicting deep convection. J. Adv. Model. Earth Syst. 11(6), 180–1784 (2019)CrossRef
16.
Washington, W.M., Parkinson, C.L.: An Introduction to Three-Dimensional Climate Modeling, University Science Books (1986)
Metadata
Title
Uncertainty Quantification for Climate Precipitation Prediction by Decision Tree
Authors
Vinicius S. Monego
Juliana A. Anochi
Haroldo F. de Campos Velho
Copyright Year
2024
Book
Proceedings of the 6th International Symposium on Uncertainty Quantification and Stochastic Modelling

Print ISBN: 978-3-031-47035-6

Electronic ISBN: 978-3-031-47036-3

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-47036-3_19

Premium Partners

    Image Credits