Predicting Cork Oak Suitability in Maâmora Forest Using Random Forest Algorithm

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Journal of Geographic Information System

ISSN Print: 2151-1950
ISSN Online: 2151-1969
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"Predicting Cork Oak Suitability in Maâmora Forest Using Random Forest Algorithm"
written by Said Lahssini, Hicham Lahlaoi, Hicham Mharzi Alaoui, El Aid Hlal, Martino Bagaram, Quentin Ponette,
published by Journal of Geographic Information System, Vol.7 No.2, 2015

has been cited by the following article(s):

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[1]	Genetic diversity and population structure in Quercus suber L. revealed by nuclear microsatellite markers and generation of a core collection Tree Genetics & Genomes, 2024 DOI:10.1007/s11295-024-01638-w

[2]	Genotypic and genetic diversity of Maâmora’s cork oak forest using simple sequence repeats (SSR) and inter simple sequence repeats (ISSR) markers for conservation Genetic Resources and Crop Evolution, 2024 DOI:10.1007/s10722-023-01774-7

[3]	Genotypic and genetic diversity of Maâmora’s cork oak forest using simple sequence repeats (SSR) and inter simple sequence repeats (ISSR) markers for conservation Genetic Resources and Crop Evolution, 2023 DOI:10.1007/s10722-023-01774-7

[4]	Spatio-temporal analysis of North African forest cover dynamics using time series of vegetation indices – case of the Maamora forest (Morocco) Biosystems Diversity, 2022 DOI:10.15421/012236

[5]	Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches Sustainability, 2022 DOI:10.3390/su14095189

[6]	Impact of climate change on potential distribution of Quercus suber in the conditions of North Africa Biosystems Diversity, 2022 DOI:10.15421/012231

[7]	Small-scale irrigation expansion along the dam-regulated Tekeze River in Northern Ethiopia International Journal of Water Resources Development, 2021 DOI:10.1080/07900627.2020.1808446

[8]	Predicting ambient traffic of a vehicle from road abrasion measurements using random forest Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 2020 DOI:10.1145/3423457.3429367

[9]	Granulometric study and phosphorus analysis from soil of Maâmora Forest (Morocco) International Journal of Hydrology, 2020 DOI:10.15406/ijh.2020.04.00247

[10]	Predicting ambient traffic of a vehicle from road abrasion measurements using random forest Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 2020 DOI:10.1145/3423457.3429367

[11]	Application of machine-learning methods in forest ecology: recent progress and future challenges Environmental Reviews, 2018 DOI:10.1139/er-2018-0034

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