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.

Machine Learning Method for Forecasting Weather Needed For Crop Water Demand Estimations in Low-Resource Settings Using A Case Study in Morocco

Domaine:

agricultureclimate

Type de record:

paper
Créateur:
CarAmo
Éditeur:
Ame
Hôte:
Abstract Low and middle income countries often do not have the infrastructure needed to support weather forecasting models, which are computationally expensive and often require detailed inputs from local weather stations. Local, low-cost weather prediction services are needed to enable optimal irrigation scheduling and increase crop productivity for rural farmers in low-resource settings. This work proposes a machine learning approach to predict the weather inputs needed to calculate crop water demand, namely evapotranspiration and precipitation. The focus of this work is on the accuracy with which Moroccan weather can be predicted with a vector autoregression (VAR) model compared to using typical meteorological year (TMY) weather, and how this accuracy changes as the number of weather parameters is reduced.

Visit

doi.org

Licenses

https://www.asme.org/publications-submissions/publishing-information/legal-policies

Similaires

Using Machine Learning for Medical Error Detection in Low-Resource SettingsQuantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare SettingsSomali dialect identification in low-resource settings using machine learning and deep learningMachine Learning Algorithm for Electricity Demand Forecasting for Improved Grid Resilience: Case Study, Senelec Network, SenegalAdapting Machine Learning Techniques for Low-Resource Settings in Developing Countries: A Multidisciplinary ApproachMachine learning-based forecasting of rainfall and water demand for urban water planning: the case of Ekurhuleni, South Africa

Using Machine Learning for Medical Error Detection in Low-Resource Settings

Abstract Medication errors during surgical procedures pose significant risks to pa

Quantized Machine Learning Models for Medical Imaging in Low-Resource Healthcare Settings

Deep learning models have shown strong performance in medical image analysis, but deploying them in

Somali dialect identification in low-resource settings using machine learning and deep learning

Abstract This study investigates automatic dialect identification for the Somali

Machine Learning Algorithm for Electricity Demand Forecasting for Improved Grid Resilience: Case Study, Senelec Network, Senegal

Adapting Machine Learning Techniques for Low-Resource Settings in Developing Countries: A Multidisciplinary Approach

Developing countries face unique challenges in harnessing the power of machine learning (ML) due to

Machine learning-based forecasting of rainfall and water demand for urban water planning: the case of Ekurhuleni, South Africa

Abstract This study develops and evaluates a data-leak-safe