Výsledky vyhledávání - "RF and KRR models"

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Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Autor: A. Yousafzai, W. Manzoor, G. Raza, T. Mahmood, F. Rehman, R. Hadi, S. Shah, M. Amin, A. Akhtar, S. Bashir, U. Habiba, M. Hussain

Publikováno v: Brazilian Journal of Biology, Vol 84 (2021)

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regr

Externí odkaz: https://doaj.org/article/bd6d31f6a2054bd9af5026acda0bcc66

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Previsão de produção florestal em diferentes cenários de mudanças climáticas usando modelos inteligentes de dados no Paquistão

Autor: Wajiha Manzoor, A. Yousafzai, Majid Hussain, M. Amin, S. Bashir, G. Raza, Fariha Rehman, R. Hadi, S. Shah, Ume Habiba, Tahir Mahmood, Andleeb Akhtar

Publikováno v: Brazilian Journal of Biology, Vol 84 (2021)
Brazilian Journal of Biology, Volume: 84, Article number: e253106, Published: 29 OCT 2021
Brazilian Journal of Biology v.84 2024
Brazilian Journal of Biology
Instituto Internacional de Ecologia (IIE)
instacron:IIE

This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (K

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6e12cecf0f598b04f3a7334fc7fd9da
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1519-69842024000100109&tlng=en

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Text‐based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons.

Autor: Fite, Shachar¹ (AUTHOR), Wahab, Alexandra² (AUTHOR), Paenurk, Eno² (AUTHOR), Gross, Zeev¹ (AUTHOR), Gershoni‐Poranne, Renana¹ (AUTHOR) rporanne@technion.ac.il

Publikováno v: Journal of Physical Organic Chemistry. Jan2023, Vol. 36 Issue 1, p1-17. 17p.

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Forecasting long-term precipitation for water resource management: a new multi-step data-intelligent modelling approach.

Autor: Ali, Mumtaz¹ (AUTHOR), Deo, Ravinesh C.² (AUTHOR) ravinesh.deo@usq.edu.au, Xiang, Yong¹ (AUTHOR), Li, Ya³ (AUTHOR), Yaseen, Zaher Mundher⁴ (AUTHOR)

Publikováno v: Hydrological Sciences Journal/Journal des Sciences Hydrologiques. Dec2020, Vol. 65 Issue 16, p2693-2708. 16p.

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Akademický článek

A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction

Autor: Hai Tao, Sinan Q. Salih, Mandeep Kaur Saggi, Esmaeel Dodangeh, Cyril Voyant, Nadhir Al-Ansari, Zaher Mundher Yaseen, Shamsuddin Shahid

Publikováno v: IEEE Access, Vol 8, Pp 83347-83358 (2020)

Accurate wind speed (WS) modelling is crucial for optimal utilization of wind energy. Numerical Weather Prediction (NWP) techniques, generally used for WS modelling are not only less cost-effective but also poor in predicting in shorter time horizon.

Externí odkaz: https://doaj.org/article/ebf283eb88d844fd8b174081ce174422

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