Mapping seagrass habitats of potential suitability using a hybrid machine learning model
Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial dist...
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ftdoajarticles:oai:doaj.org/article:889ef11e44454aa387324f55fa6a3b14 2023-05-15T17:51:32+02:00 Mapping seagrass habitats of potential suitability using a hybrid machine learning model Bohao He Yanghe Zhao Siyu Liu Shahid Ahmad Wei Mao 2023-02-01T00:00:00Z https://doi.org/10.3389/fevo.2023.1116083 https://doaj.org/article/889ef11e44454aa387324f55fa6a3b14 EN eng Frontiers Media S.A. https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083/full https://doaj.org/toc/2296-701X 2296-701X doi:10.3389/fevo.2023.1116083 https://doaj.org/article/889ef11e44454aa387324f55fa6a3b14 Frontiers in Ecology and Evolution, Vol 11 (2023) seagrass machine learning species distribution model hybrid model habitat suitability niches Evolution QH359-425 Ecology QH540-549.5 article 2023 ftdoajarticles https://doi.org/10.3389/fevo.2023.1116083 2023-02-05T01:28:09Z Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems. Article in Journal/Newspaper Ocean acidification Directory of Open Access Journals: DOAJ Articles Frontiers in Ecology and Evolution 11 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
seagrass machine learning species distribution model hybrid model habitat suitability niches Evolution QH359-425 Ecology QH540-549.5 |
spellingShingle |
seagrass machine learning species distribution model hybrid model habitat suitability niches Evolution QH359-425 Ecology QH540-549.5 Bohao He Yanghe Zhao Siyu Liu Shahid Ahmad Wei Mao Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
topic_facet |
seagrass machine learning species distribution model hybrid model habitat suitability niches Evolution QH359-425 Ecology QH540-549.5 |
description |
Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems. |
format |
Article in Journal/Newspaper |
author |
Bohao He Yanghe Zhao Siyu Liu Shahid Ahmad Wei Mao |
author_facet |
Bohao He Yanghe Zhao Siyu Liu Shahid Ahmad Wei Mao |
author_sort |
Bohao He |
title |
Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
title_short |
Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
title_full |
Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
title_fullStr |
Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
title_full_unstemmed |
Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
title_sort |
mapping seagrass habitats of potential suitability using a hybrid machine learning model |
publisher |
Frontiers Media S.A. |
publishDate |
2023 |
url |
https://doi.org/10.3389/fevo.2023.1116083 https://doaj.org/article/889ef11e44454aa387324f55fa6a3b14 |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Frontiers in Ecology and Evolution, Vol 11 (2023) |
op_relation |
https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083/full https://doaj.org/toc/2296-701X 2296-701X doi:10.3389/fevo.2023.1116083 https://doaj.org/article/889ef11e44454aa387324f55fa6a3b14 |
op_doi |
https://doi.org/10.3389/fevo.2023.1116083 |
container_title |
Frontiers in Ecology and Evolution |
container_volume |
11 |
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1766158708211974144 |