Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas
For hydrological forecasting in karst areas,existing research mainly uses hydrological models based on physical mechanisms,while rare research focuses on machine learning models.To explore the applicability of machine learning models in karst areas, this paper utilizes the LSTM model and random fore...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Pearl River
2024-01-01
|
| Series: | Renmin Zhujiang |
| Subjects: | |
| Online Access: | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.03.007 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841530680835571712 |
|---|---|
| author | ZHAO Zejin SUN Wei ZHOU Bin ZHANG Xuan WANG Gaoxu WU Wei LI Wenjie YAO Ye |
| author_facet | ZHAO Zejin SUN Wei ZHOU Bin ZHANG Xuan WANG Gaoxu WU Wei LI Wenjie YAO Ye |
| author_sort | ZHAO Zejin |
| collection | DOAJ |
| description | For hydrological forecasting in karst areas,existing research mainly uses hydrological models based on physical mechanisms,while rare research focuses on machine learning models.To explore the applicability of machine learning models in karst areas, this paper utilizes the LSTM model and random forest model to simulate the daily runoff and field floods at Tangdian hydrological station,using the Shadian River basin in Yunnan Province as the study area.The modified Xin'anjiang model for karst areas is taken as a reference.The results show that both the machine learning model and the modified Xin'anjiang model have achieved good results in simulating the daily runoff process, with the LSTM model showing better simulation results.In the simulation of floods,the modified Xin'anjiang model achieves Class A forecast accuracy.The machine learning models have better forecast results for the 6-hour forecasting period than the modified Xin'anjiang model,while the forecast results for the 24-hour forecasting period do not meet the accuracy requirements of the forecast operation.The study provides a reference for hydrological forecasting in karst areas by studying the characteristics and forecasting accuracy of two machine learning models and a hydrologic model. |
| format | Article |
| id | doaj-art-853eaa53622e46d09c2209bd3af05c2a |
| institution | Kabale University |
| issn | 1001-9235 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Pearl River |
| record_format | Article |
| series | Renmin Zhujiang |
| spelling | doaj-art-853eaa53622e46d09c2209bd3af05c2a2025-01-15T03:00:41ZzhoEditorial Office of Pearl RiverRenmin Zhujiang1001-92352024-01-014554032236Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst AreasZHAO ZejinSUN WeiZHOU BinZHANG XuanWANG GaoxuWU WeiLI WenjieYAO YeFor hydrological forecasting in karst areas,existing research mainly uses hydrological models based on physical mechanisms,while rare research focuses on machine learning models.To explore the applicability of machine learning models in karst areas, this paper utilizes the LSTM model and random forest model to simulate the daily runoff and field floods at Tangdian hydrological station,using the Shadian River basin in Yunnan Province as the study area.The modified Xin'anjiang model for karst areas is taken as a reference.The results show that both the machine learning model and the modified Xin'anjiang model have achieved good results in simulating the daily runoff process, with the LSTM model showing better simulation results.In the simulation of floods,the modified Xin'anjiang model achieves Class A forecast accuracy.The machine learning models have better forecast results for the 6-hour forecasting period than the modified Xin'anjiang model,while the forecast results for the 24-hour forecasting period do not meet the accuracy requirements of the forecast operation.The study provides a reference for hydrological forecasting in karst areas by studying the characteristics and forecasting accuracy of two machine learning models and a hydrologic model.http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.03.007karst areaflood forecastingmachine learningmodified Xin'anjiang model |
| spellingShingle | ZHAO Zejin SUN Wei ZHOU Bin ZHANG Xuan WANG Gaoxu WU Wei LI Wenjie YAO Ye Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas Renmin Zhujiang karst area flood forecasting machine learning modified Xin'anjiang model |
| title | Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas |
| title_full | Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas |
| title_fullStr | Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas |
| title_full_unstemmed | Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas |
| title_short | Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas |
| title_sort | applicability analysis of machine learning model in hydrological forecasting in karst areas |
| topic | karst area flood forecasting machine learning modified Xin'anjiang model |
| url | http://www.renminzhujiang.cn/thesisDetails#10.3969/j.issn.1001-9235.2024.03.007 |
| work_keys_str_mv | AT zhaozejin applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT sunwei applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT zhoubin applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT zhangxuan applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT wanggaoxu applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT wuwei applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT liwenjie applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas AT yaoye applicabilityanalysisofmachinelearningmodelinhydrologicalforecastinginkarstareas |