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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Online Access: | http://dx.doi.org/10.3389/fevo.2023.1116083 https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083/full |
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crfrontiers:10.3389/fevo.2023.1116083 2024-03-31T07:54:46+00:00 Mapping seagrass habitats of potential suitability using a hybrid machine learning model He, Bohao Zhao, Yanghe Liu, Siyu Ahmad, Shahid Mao, Wei Major Science and Technology Project of Hainan Province National Natural Science Foundation of China Hainan University 2023 http://dx.doi.org/10.3389/fevo.2023.1116083 https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083/full unknown Frontiers Media SA https://creativecommons.org/licenses/by/4.0/ Frontiers in Ecology and Evolution volume 11 ISSN 2296-701X Ecology Ecology, Evolution, Behavior and Systematics journal-article 2023 crfrontiers https://doi.org/10.3389/fevo.2023.1116083 2024-03-05T00:17:08Z 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 Frontiers (Publisher) Frontiers in Ecology and Evolution 11 |
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topic |
Ecology Ecology, Evolution, Behavior and Systematics |
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Ecology Ecology, Evolution, Behavior and Systematics He, Bohao Zhao, Yanghe Liu, Siyu Ahmad, Shahid Mao, Wei Mapping seagrass habitats of potential suitability using a hybrid machine learning model |
topic_facet |
Ecology Ecology, Evolution, Behavior and Systematics |
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. |
author2 |
Major Science and Technology Project of Hainan Province National Natural Science Foundation of China Hainan University |
format |
Article in Journal/Newspaper |
author |
He, Bohao Zhao, Yanghe Liu, Siyu Ahmad, Shahid Mao, Wei |
author_facet |
He, Bohao Zhao, Yanghe Liu, Siyu Ahmad, Shahid Mao, Wei |
author_sort |
He, Bohao |
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 SA |
publishDate |
2023 |
url |
http://dx.doi.org/10.3389/fevo.2023.1116083 https://www.frontiersin.org/articles/10.3389/fevo.2023.1116083/full |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
Frontiers in Ecology and Evolution volume 11 ISSN 2296-701X |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/fevo.2023.1116083 |
container_title |
Frontiers in Ecology and Evolution |
container_volume |
11 |
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1795035965407887360 |