Ashland City, Tennessee, located within the Lower Cumberland Sycamore watershed, is highly susceptible to flooding due to increased upstream water levels. This study aimed to develop a robust flood prediction model for the city, utilizing water level data at 30-minute intervals from ten USGS gauge stations within the watershed. A Gated Recurrent Unit (GRU) network, known for its ability to effectively process sequential time-series data, was used. The model was trained, validated, and tested using a year-long dataset (January 2021-January 2022), and its performance was evaluated using statistical metrics including Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Percent Bias (PBIAS), Mean Absolute Error (MAE), and Coefficient of Determination (R^2). The results demonstrated a high level of accuracy, with the model explaining 98.2% of the variance in the data. Despite minor discrepancies between predicted and observed values, the GRU model proved to be an effective tool for flood prediction in Ashland City, with potential applications for enhancing disaster preparedness and response efforts in Ashland City.

为阿什兰德城开发了一种可靠的洪水预测模型，使用游击递归单元（GRU）网络处理与时间序列相关的水位数据，在统计指标（如Nash-Sutcliffe效率、均方根误差、百分偏差、平均绝对误差和确定系数）的评估下，该模型表现出高水平的准确性，对数据方差解释率为98.2%。尽管存在预测值与观测值之间的轻微差异，但GRU模型证明在阿什兰德城的洪水预测中是一种有效的工具，可用于增强该城市的灾害应对和准备工作。

田纳西州阿什兰市洪水预测的GRU神经网络实现