Using Random Forest for Future Sea Level Prediction
This research paper presents an investigation into using the random forest algorithm for predicting future sea level. Sea level is a critical indicator of the health of our oceans and coastal areas and is measured in total weight observations. The study employs the random forest algorithm, a powerfu...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | SHS Web of Conferences |
| Online Access: | https://www.shs-conferences.org/articles/shsconf/pdf/2023/23/shsconf_seaa2023_03008.pdf |
| _version_ | 1797740194879766528 |
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| author | Ding Haolun |
| author_facet | Ding Haolun |
| author_sort | Ding Haolun |
| collection | DOAJ |
| description | This research paper presents an investigation into using the random forest algorithm for predicting future sea level. Sea level is a critical indicator of the health of our oceans and coastal areas and is measured in total weight observations. The study employs the random forest algorithm, a powerful machine learning technique, to analyze a dataset of sea level observations. The results of the analysis demonstrate the effectiveness of the random forest algorithm in accurately predicting future sea level changes. The findings of this research have important implications for coastal management and adaptation strategies. This research provides a valuable tool for decision-makers and coastal managers, allowing for more informed and proactive planning for sea level rise. Overall, the paper shows that the random forest algorithm is a promising method for sea level prediction and highlights the importance of continued research in this area. |
| first_indexed | 2024-03-12T14:08:57Z |
| format | Article |
| id | doaj.art-ae4c1feeaaa44fa6aff806ed39faeae3 |
| institution | Directory Open Access Journal |
| issn | 2261-2424 |
| language | English |
| last_indexed | 2024-03-12T14:08:57Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | SHS Web of Conferences |
| spelling | doaj.art-ae4c1feeaaa44fa6aff806ed39faeae32023-08-21T09:05:40ZengEDP SciencesSHS Web of Conferences2261-24242023-01-011740300810.1051/shsconf/202317403008shsconf_seaa2023_03008Using Random Forest for Future Sea Level PredictionDing Haolun0Wenshan Middle SchoolThis research paper presents an investigation into using the random forest algorithm for predicting future sea level. Sea level is a critical indicator of the health of our oceans and coastal areas and is measured in total weight observations. The study employs the random forest algorithm, a powerful machine learning technique, to analyze a dataset of sea level observations. The results of the analysis demonstrate the effectiveness of the random forest algorithm in accurately predicting future sea level changes. The findings of this research have important implications for coastal management and adaptation strategies. This research provides a valuable tool for decision-makers and coastal managers, allowing for more informed and proactive planning for sea level rise. Overall, the paper shows that the random forest algorithm is a promising method for sea level prediction and highlights the importance of continued research in this area.https://www.shs-conferences.org/articles/shsconf/pdf/2023/23/shsconf_seaa2023_03008.pdf |
| spellingShingle | Ding Haolun Using Random Forest for Future Sea Level Prediction SHS Web of Conferences |
| title | Using Random Forest for Future Sea Level Prediction |
| title_full | Using Random Forest for Future Sea Level Prediction |
| title_fullStr | Using Random Forest for Future Sea Level Prediction |
| title_full_unstemmed | Using Random Forest for Future Sea Level Prediction |
| title_short | Using Random Forest for Future Sea Level Prediction |
| title_sort | using random forest for future sea level prediction |
| url | https://www.shs-conferences.org/articles/shsconf/pdf/2023/23/shsconf_seaa2023_03008.pdf |
| work_keys_str_mv | AT dinghaolun usingrandomforestforfuturesealevelprediction |