Modeling a Potential Plant Habitat by Ensemble Machine Learning

Original title

Modeling a Potential Plant Habitat by Ensemble Machine Learning

Russian translation

Моделирование потенциального ареала обитания растений методами ансамблевого машинного обучения

Translation status: source

Evidence statuses

relation_layer

D — context

relation_confidence

2 — strong

relevance_status

Included

body_status

Partial text or abstract

doi_status

Confirmed

access_status

Metadata only

expert_review

Included

verification_status

Metadata verified

Details

year

2023

type

Book chapter

venue

High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production: HPCST 2022, Revised Selected Papers; Communications in Computer and Information Science

DOI

10.1007/978-3-031-23744-7_16

People

• Ваганов Алексей Владимирович
• Зайков Владимир Федорович
• Кротова Ольга Сергеевна
• Мусохранов Андрей Игоревич
• Покалякин Захар Валерьевич
• Хворова Любовь Анатольевна

Topics

• Agrometeorology
• Calibration and validation
• GIS and remote sensing
• Model comparison and ensembles

Sources

• Springer official chapter metadata and abstract: potential plant habitat by ensemble machine learning, 2023

External links

• https://doi.org/10.1007/978-3-031-23744-7_16
• https://link.springer.com/chapter/10.1007/978-3-031-23744-7_16