Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

African Q99 prediction model: Hydrological clustering and regression machine learning for extreme flow prediction in African catchments

Créateur:
MaeFarMim
Éditeur:
Elsevier BV
Hôte:

Visit

doi.org

Licenses

https://www.elsevier.com/tdm/userlicense/1.0/https://www.elsevier.com/legal/tdmrep-licensehttps://doi.org/10.15223/policy-017https://doi.org/10.15223/policy-037https://doi.org/10.15223/policy-012https://doi.org/10.15223/policy-029https://doi.org/10.15223/policy-004

Similaires

Machine Learning for AMR prediction in African PathogensAjasaHameed/Machine-Learning-Prediction-Symbolic-RegressionHydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, AlgeriaGlobal solar radiation prediction using hybrid online sequential extreme learning machine modelMachine Learning Model Approaches for Price Prediction in Coffee Market using Linear Regression, XGB, and LSTM TechniquesDevelopment of a machine learning regression model for accurate sugarcane crop yield prediction, Jinja – Uganda

Machine Learning for AMR prediction in African Pathogens

This study aims to develop and validate machine learning models for predicting antimicrobial resista

AjasaHameed/Machine-Learning-Prediction-Symbolic-Regression

In this case, a novel approach: symbolic regression is used to predicts the prices of Nigeria common

Hydrological Drought Prediction Based on Hybrid Extreme Learning Machine: Wadi Mina Basin Case Study, Algeria

Drought is one of the most severe climatic calamities, affecting many aspects of the environment and

Global solar radiation prediction using hybrid online sequential extreme learning machine model

Accurate global solar radiation prediction is highly essential for related research on renewable ene

Machine Learning Model Approaches for Price Prediction in Coffee Market using Linear Regression, XGB, and LSTM Techniques

Investors and other business persons have a desire to know about the future market price because, if

Development of a machine learning regression model for accurate sugarcane crop yield prediction, Jinja – Uganda

Sugarcane is one of the key crops grown worldwide and used for sugar processing, food, alcohol, biog