He, B.; Zhao, Y.; Liu, S.; Ahmad, S.; Mao, W. Mapping Seagrass Habitats of Potential Suitability Using a Hybrid Machine Learning Model. Frontiers in Ecology and Evolution 2023, 11, doi:10.3389/fevo.2023.1116083.
He, B.; Zhao, Y.; Liu, S.; Ahmad, S.; Mao, W. Mapping Seagrass Habitats of Potential Suitability Using a Hybrid Machine Learning Model. Frontiers in Ecology and Evolution 2023, 11, doi:10.3389/fevo.2023.1116083.
He, B.; Zhao, Y.; Liu, S.; Ahmad, S.; Mao, W. Mapping Seagrass Habitats of Potential Suitability Using a Hybrid Machine Learning Model. Frontiers in Ecology and Evolution 2023, 11, doi:10.3389/fevo.2023.1116083.
He, B.; Zhao, Y.; Liu, S.; Ahmad, S.; Mao, W. Mapping Seagrass Habitats of Potential Suitability Using a Hybrid Machine Learning Model. Frontiers in Ecology and Evolution 2023, 11, doi:10.3389/fevo.2023.1116083.
Abstract
Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.
Keywords
seagrass; remote sensing; machine learning; species distribution model (SDM); hybrid model; habitat suitability; niches; meta-heuristic optimization
Subject
Environmental and Earth Sciences, Oceanography
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
