Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning
Sea surface temperature (SST) is one of the main factors for drought forecasting. Conventional forecasting models mainly use SST from fixed sea areas (e.g., ENSO), without searching for available SST signals from a global large-scale perspective. Combining with the random forest algorithm, this pape...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2024-05-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.05.011 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841530584654938112 |
|---|---|
| author | FENG Xin LIU Yanju TONG Hongfu QIAN Shuni |
| author_facet | FENG Xin LIU Yanju TONG Hongfu QIAN Shuni |
| author_sort | FENG Xin |
| collection | DOAJ |
| description | Sea surface temperature (SST) is one of the main factors for drought forecasting. Conventional forecasting models mainly use SST from fixed sea areas (e.g., ENSO), without searching for available SST signals from a global large-scale perspective. Combining with the random forest algorithm, this paper constructs a new meteorological drought forecasting model through regression analysis to screen global SST areas of forecasting significance and takes the Pearl River Basin as an example for application tests. The results are as follows. ① The model can effectively forecast the temporal and spatial evolution of drought, and as the forecast period becomes longer, the forecast accuracy decreases accordingly. ② The accuracy of drought forecast is higher in non-flood season than in flood season, and the coastal area has a better forecast effect than the inland area. ③ The occurrence of droughts in the Pearl River Basin may be related to typical climate fluctuations, such as the El Niño Southern Oscillation and the North Atlantic Oscillation. |
| format | Article |
| id | doaj-art-44d849e12d4942c9ad052274b576cc07 |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-05-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-44d849e12d4942c9ad052274b576cc072025-01-15T03:00:57ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-05-01459610259362745Drought Prediction Model of Pearl River Basin Based on SST and Machine LearningFENG XinLIU YanjuTONG HongfuQIAN ShuniSea surface temperature (SST) is one of the main factors for drought forecasting. Conventional forecasting models mainly use SST from fixed sea areas (e.g., ENSO), without searching for available SST signals from a global large-scale perspective. Combining with the random forest algorithm, this paper constructs a new meteorological drought forecasting model through regression analysis to screen global SST areas of forecasting significance and takes the Pearl River Basin as an example for application tests. The results are as follows. ① The model can effectively forecast the temporal and spatial evolution of drought, and as the forecast period becomes longer, the forecast accuracy decreases accordingly. ② The accuracy of drought forecast is higher in non-flood season than in flood season, and the coastal area has a better forecast effect than the inland area. ③ The occurrence of droughts in the Pearl River Basin may be related to typical climate fluctuations, such as the El Niño Southern Oscillation and the North Atlantic Oscillation.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.05.011drought predicationsea surface temperatureRandom ForestPearl River Basin |
| spellingShingle | FENG Xin LIU Yanju TONG Hongfu QIAN Shuni Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning Renmin Zhujiang drought predication sea surface temperature Random Forest Pearl River Basin |
| title | Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning |
| title_full | Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning |
| title_fullStr | Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning |
| title_full_unstemmed | Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning |
| title_short | Drought Prediction Model of Pearl River Basin Based on SST and Machine Learning |
| title_sort | drought prediction model of pearl river basin based on sst and machine learning |
| topic | drought predication sea surface temperature Random Forest Pearl River Basin |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.05.011 |
| work_keys_str_mv | AT fengxin droughtpredictionmodelofpearlriverbasinbasedonsstandmachinelearning AT liuyanju droughtpredictionmodelofpearlriverbasinbasedonsstandmachinelearning AT tonghongfu droughtpredictionmodelofpearlriverbasinbasedonsstandmachinelearning AT qianshuni droughtpredictionmodelofpearlriverbasinbasedonsstandmachinelearning |