刊名 | 《现代化工》 | ||||
作者 | Jia Li 1,Bin Hu 2,Shiyu Mou 1,Canlin Xiong 1,Yuanhao Fang 2 (1.Department of Hydrology and Water Resources,Hohai University,China;2.National Energy Dadu River Basin Hydropower Development Co.,Ltd.,China) | 英文名 | Modern Chemical Industry | 年,卷(期) | 2024年,第12期 |
主办单位 | 环宇科学出版社 | 刊号 | ISSN:2661-3670(P)/2661-3689(O) | DOI |
Precipitation forecasting holds significant importance in fields such as disaster prevention,mitigation,and water resource management.However,existing precipitation forecasts often encounter issues such as spatial bias and inadequate predictions of extreme precipitation events.This paper proposes a correction model that combines statistical methods with deep learning,named Unet-DConvLSTM-QM (U-D-Q).The model optimizes precipitation spatial distribution using Unet,employs a dual-layer ConvLSTM to distinguish between rain/no-rain and correct precipitation intensity,and finally applies Quantile Mapping (QM)for further forecast adjustment.Focusing on the Dadu River Basin,the study compares U-D-Q with quantile mapping and ConvLSTM,systematically evaluating its performance.The study's results demonstrate that the U-D-Q model outperforms other methods (quantile mapping and ConvLSTM)in key metrics such as the Critical Success Index (CSI),Mean Absolute Error (MAE),Root Mean Square Error (RMSE),and Correlation Coefficient (CC).It excels particularly in the medium to high-intensity precipitation intervals ([10,25), [25,50),[50,+∞)),significantly reducing errors in extreme precipitation events.While the QM model performs well in low-intensity precipitation intervals ([0.1,10)),its adaptability to extreme events is limited.Although ConvLSTM shows some improvements,it does not match the overall effectiveness of the U-D-Q model.This research provides a novel approach to enhancing precipitation forecast accuracy and validates the potential of deep learning applications in precipitation forecasting.
营业时间:9;00-11:30 13:30-17:00
地址:总部:香港湾仔骆克道315-321号幸运广场23楼C室;分部:香港九龍新蒲崗太子道東704號新時代工貿商業中心31樓5-11室A03單位
邮箱:gjkzxtg@126.com
客服QQ:3577400288