A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand
Beach slope is a critical parameter for understanding coastal geomorphological dynamics, yet the acquisition of comprehensive datasets at large scales remains a significant challenge. This study bridges this gap by presenting a novel methodology for estimating beach slopes across New Zealand’s sandy...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-07-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2025.2522142 |
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| author | Hao Xu Nan Xu Chi Zhang Shanhang Chi Yuan Li Wenyu Li Yifu Ou Jiaqi Yao Han-Su Zhang Fan Mo Hui Lu |
| author_facet | Hao Xu Nan Xu Chi Zhang Shanhang Chi Yuan Li Wenyu Li Yifu Ou Jiaqi Yao Han-Su Zhang Fan Mo Hui Lu |
| author_sort | Hao Xu |
| collection | DOAJ |
| description | Beach slope is a critical parameter for understanding coastal geomorphological dynamics, yet the acquisition of comprehensive datasets at large scales remains a significant challenge. This study bridges this gap by presenting a novel methodology for estimating beach slopes across New Zealand’s sandy coastlines. We developed robust coastal slope estimation models for sandy beaches by integrating 12 environmental factors with high-precision LiDAR-derived slope data, employing four machine learning regression techniques: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). These models were trained on datasets from 1,241 beaches with LiDAR-derived Digital Elevation Models (DEMs) and subsequently applied to predict coastal slopes for an additional 509 beaches lacking LiDAR data. The results reveal that the XGBoost model outperformed the others, achieving the highest accuracy with an R2 of 0.93 and an MAE of 0.02, demonstrating the effectiveness of machine learning in coastal slope estimation. This innovative approach, leveraging DEM datasets and environmental variables, provides a robust and cost-effective tool for estimating coastal slopes across global sandy beaches compared to high-cost field measurement methods. We also emphasized that our method can estimate beach slopes for beaches without topography data based on constructed machine learning methods and environmental factors. Future studies should focus on incorporating additional environmental covariates, and extending the model’s applicability to diverse coastal environments, thereby enhancing its predictive accuracy and utility, supporting sustainable coastal development worldwide. |
| format | Article |
| id | doaj-art-1209fea4c75a47bc807440da3eafed0a |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-1209fea4c75a47bc807440da3eafed0a2025-08-20T03:28:58ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-07-0112010.1080/10095020.2025.2522142A machine learning based estimation method of beach slopes at a national scale: a case study of New ZealandHao Xu0Nan Xu1Chi Zhang2Shanhang Chi3Yuan Li4Wenyu Li5Yifu Ou6Jiaqi Yao7Han-Su Zhang8Fan Mo9Hui Lu10School of Earth Sciences and Engineering, Hohai University, Nanjing, ChinaSchool of Earth Sciences and Engineering, Hohai University, Nanjing, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, ChinaThe National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, ChinaDepartment of Geography and Planning, University of Toronto, Toronto, ON, CanadaDepartment of Geography, Hong Kong Baptist University, Hong Kong, ChinaAcademy of Ecological Civilization Development for JING-JIN-JI Megalopolis, Tianjin Normal University, Tianjin, ChinaSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, ChinaLand Satellite Remote Sensing Application Center (LASAC), Ministry of Natural Resources of PR China, Beijing, ChinaDepartment of Earth System Science, Institute for Global Change Studies, State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing, ChinaBeach slope is a critical parameter for understanding coastal geomorphological dynamics, yet the acquisition of comprehensive datasets at large scales remains a significant challenge. This study bridges this gap by presenting a novel methodology for estimating beach slopes across New Zealand’s sandy coastlines. We developed robust coastal slope estimation models for sandy beaches by integrating 12 environmental factors with high-precision LiDAR-derived slope data, employing four machine learning regression techniques: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). These models were trained on datasets from 1,241 beaches with LiDAR-derived Digital Elevation Models (DEMs) and subsequently applied to predict coastal slopes for an additional 509 beaches lacking LiDAR data. The results reveal that the XGBoost model outperformed the others, achieving the highest accuracy with an R2 of 0.93 and an MAE of 0.02, demonstrating the effectiveness of machine learning in coastal slope estimation. This innovative approach, leveraging DEM datasets and environmental variables, provides a robust and cost-effective tool for estimating coastal slopes across global sandy beaches compared to high-cost field measurement methods. We also emphasized that our method can estimate beach slopes for beaches without topography data based on constructed machine learning methods and environmental factors. Future studies should focus on incorporating additional environmental covariates, and extending the model’s applicability to diverse coastal environments, thereby enhancing its predictive accuracy and utility, supporting sustainable coastal development worldwide.https://www.tandfonline.com/doi/10.1080/10095020.2025.2522142Beachcoastal slopeLiDARmachine learningsea level rise |
| spellingShingle | Hao Xu Nan Xu Chi Zhang Shanhang Chi Yuan Li Wenyu Li Yifu Ou Jiaqi Yao Han-Su Zhang Fan Mo Hui Lu A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand Geo-spatial Information Science Beach coastal slope LiDAR machine learning sea level rise |
| title | A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand |
| title_full | A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand |
| title_fullStr | A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand |
| title_full_unstemmed | A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand |
| title_short | A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand |
| title_sort | machine learning based estimation method of beach slopes at a national scale a case study of new zealand |
| topic | Beach coastal slope LiDAR machine learning sea level rise |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2025.2522142 |
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