To improve streamflow simulation, a coupled SWAT-LSTM model was constructed by combining a conceptual process-based hydrological model—Soil and Water Assessment Tool (SWAT)—with a machine learning model—Long Short-Term Memory (LSTM). The coupled model was applied to simulate the daily streamflow...
本发明提供一种基于LSTM‑SWAT耦合模型的洪水径流量预报方法,包括:构建SWAT分布式水文模型,并分析SWAT分布式水文模型率定及参数敏感性;引入BiLSTM深度学习模型,并耦合构建的SWAT分布式水文模型与BiLSTM深度学习模型,基于SWAT分布式水文模型的物理机制运算,在空间与时间上完成对输入气象数据的扩展;将SWAT分布式水文模型输出的...
LSTM模型分别提高了 0.04和0.01.使用交叉验证方法在不同预测长度下的平均R2与平均NSE分别为0.79和0.64,较LSTM模型分别提高了0.11和0.09.分析SWAT模型较LSTM模型月径流模拟精度更高的原因,一方面由于SWAT模型径流模拟结果与降雨量相关性更强,使用相关性分析,SWAT径流模拟结果与降雨量之间的Pearson相关系数为0.82,高于LSTM...
Additionally, coupled models using different SWAT+ hydrological outputs and meteorological data as LSTM input data outperformed those using only SWAT+ estimated streamflow. This improvement was more notable in scenarios combining LSTM and default SWAT+ models, highlighting the SWAT+ default model's ...
LSTM models do not overestimate the high flows like SWAT. However, both these models struggle with low values estimation. Although interpretability, explainability, and use of models across different datasets or events outside of the training data may be challenging, LSTM models are robust and ...
rainfall runoff modeling; streamflow; SWAT; LSTM; physically based; data driven1. Introduction The history of rainfall–runoff modeling is among the oldest yet evolving in hydrological sciences. Initially, attempts were made to simulate and predict discharge based on precipitation events using regression...