耦合区间概率计算方法的深度学习洪水预报模型研究

Research on deep learning flood forecasting model coupled with interval probability calculation method

摘要

【目的】准确可靠的洪水预报是防洪减灾的重要的非工程措施之一，结合深度学习智能技术提高故县水库入库洪水预报精度和可靠性对防洪调度决策有重要意义。【方法】通过在Transformer模型输出层耦合Bootstrap区间预测计算方法，构建一种深度学习洪水过程概率预报模型Transformer-Bootstrap。基于故县水库入库洪水监测站卢氏水文站控制流域内1990—2016年间实测降雨-径流数据，使用Chapman滤波法进行基流分割得到49场逐小时的洪水事件数据，其中1990—2010年间39场洪水用于训练，2011—2016年间10场洪水用于验证。同时选用纳什效率系数(NSE)、均方根误差(RMSE)、偏差(bias)和决定性系数(R²)进行模型性能评估。【结果】结果表明：1~6 h预见期条件下，训练期预报NSE从0.98下降至0.92、验证期预报NSE从0.89下降至0.55;预报误差呈增加趋势，训练期RMSE和bias分别从31.54 m³/s增加至64.04 m³/s、9.08%增加至24.05%,验证期预报RMSE和bias分别从11.01 m³/s增加至20.57 m³/s、3.90%增加至11.36%。同时迭代计算次数75次左右模型升至超过175次模型才趋向收敛。验证期对应预报PIPC值从89.5%下降至61.6%,预报PINAW从0.008增长至0.076。尤其是当预见期增加至4~6 h时，预报流量和观测流量偏差较大，预报效果越差。【结论】Transformer-Bootstrap模型在1~3 h短预见期条件下，训练期和验证期预报NSE超过0.80,预报效果较好；Transformer-Bootstrap模型概率预测覆盖率基本超过或接近所对应90%置信水平的置信区间，概率预报结果是相对合理可靠；随着预见期增加，Transformer-Bootstrap模型的预报精度呈下降趋势，预报误差呈上升趋势；同时模型需要更多的迭代计算次数才能收敛，模型训练期的预报精度和稳定性优于验证期，未来如何提高长预见期条件下的深度学习模型洪水预报精度和鲁棒性是值得深入研究的关键科学问题，研究成果可为故县水库及黄河中游部分地区防洪减灾提供技术支持。

Abstract

[Objective] Accurate and reliable flood forecasting is one of the important non-engineering measures for flood control and disaster reduction. Improving the accuracy and reliability of inflow flood forecasting for the Guxian Reservoir using deep learning technologies is of great importance for flood control and dispatch decision-making. [Methods] A deep learning-based flood process probabilistic forecasting model, Transformer-Bootstrap, was constructed by coupling the output layer of the Transformer model with the Bootstrap interval prediction method. Using the observed rainfall-runoff data from the Lushi Hydrological Station, which controls the catchment area of the Guxian Reservoir for the period 1990—2016, and applying the Chapman filtering method for baseflow separation, 49 hourly flood event datasets were obtained. Of these, 39 flood events from 1990 to 2010 were used for training, while 10 flood events from 2011 to 2016 were used for validation. The model's performance was evaluated using metrics such as Nash-Sutcliffe Efficiency(NSE), Root Mean Square Error(RMSE), bias, and Coefficient of Determination(R²). [Results] The result showed that for forecast lead times of 1~6 hours, the NSE for the training period decreased from 0.98 to 0.92, while the NSE for the validation period decreased from 0.89 to 0.55. Forecast errors showed an increasing trend, with RMSE and bias for the training period increasing from 31.54 m³/s to 64.04 m³/s and from 9.08% to 24.05%, respectively, while RMSE and bias for the validation period increased from 11.01 m³/s to 20.57 m³/s and from 3.90% to 11.36%, respectively. The model began to improve after approximately 75 iterations and only approached convergence after more than 175 iterations. During the validation period, the PIPC value of the forecast decreased from 89.5% to 61.6%, and the PINAW value increased from 0.008 to 0.076. Particularly, as the forecast lead time increased to 4~6 hours, the bias between the forecast and observed discharge became larger, leading to poorer forecast performance. [Conclusion] The Transformer-Bootstrap model demonstrates good performance for short lead times(1~3 hours), with NSE values exceeding 0.80 for both the training and validation periods. The probabilistic forecast coverage for the Transformer-Bootstrap model generally exceeds or is close to the 90% confidence level interval, and the probabilistic forecast result are reasonably reliable. However, as the lead time increases, the forecast accuracy decreases, and forecast errors increase. Additionally, the model requires more iterative calculations to converge, with the forecast accuracy and stability during the training period superior to those during the validation period. A key scientific issue for future research is how to improve the accuracy and robustness of deep learning flood forecasting models for longer lead times. The findings provide technical support for flood control and disaster reduction in the Guxian Reservoir and parts of the middle reaches of the Yellow River.

关键词

洪水预报 / 深度学习 / 概率预测 / Bootstrap区间预测方法 / Transformer模型 / 故县水库 / 预见期 / 防洪减灾

Key words

flood forecasting / deep learning / probabilistic prediction / Bootstrap interval prediction method / Transformer model / Guxian Reservoir / lead-time / flood control and disaster reduction

基金资助

国家自然科学基金项目(52039006)

达州市科技计划项目(21ZDYF0016)