Machine Learning-Based Flood Forecasting System for Window Cliffs State Natural Area, Tennessee

George K. Darkwah, A. Kalyanapu, Collins Owusu
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Abstract

The prevalence of unforeseen floods has heightened the need for more accurate flood simulation and forecasting models. Even though forecast stations are expanding across the United States, the coverage is usually limited to major rivers and urban areas. Most rural and sub-urban areas, including recreational areas such as the Window Cliffs State Natural Area, do not have such forecast stations and as such, are prone to the dire effects of unforeseen flooding. In this study, four machine learning model architectures were developed based on the long short-term memory, random forest, and support vector regression techniques to forecast water depths at the Window Cliffs State Natural Area, located within the Cane Creek watershed in Putnam County, Tennessee. Historic upstream and downstream water levels and absolute pressure were used to forecast the future water levels downstream of the Cane Creek watershed. The models were tested with lead times of 3, 4, 5, and 6 h, revealing that the model performances reduced with an increase in lead time. Even though the models yielded low errors of 0.063–0.368 ft MAE, there was an apparent delay in predicting the peak water depths. However, including rainfall data in the forecast showed a promising improvement in the models’ performance. Tests conducted on the Cumberland River in Tennessee showed a promising improvement in model performance when trained with larger data.
田纳西州窗口崖国家自然区基于机器学习的洪水预报系统
不可预见的洪水频发,更加需要更准确的洪水模拟和预报模型。尽管预报站在美国各地不断扩大,但其覆盖范围通常仅限于主要河流和城市地区。大多数农村和城郊地区,包括窗崖州立自然区等休闲区,都没有这样的预报站,因此很容易受到不可预见的洪水的严重影响。在这项研究中,基于长短期记忆、随机森林和支持向量回归技术开发了四种机器学习模型架构,用于预报田纳西州普特南县 Cane Creek 流域内 Window Cliffs 州立自然区的水深。历史上的上下游水位和绝对压力被用来预测 Cane Creek 流域下游的未来水位。模型在 3、4、5 和 6 小时的准备时间内进行了测试,结果表明,随着准备时间的增加,模型的性能也随之降低。尽管模型的 MAE 误差较低,为 0.063-0.368 英尺,但在预测峰值水深方面存在明显的延迟。不过,在预测中加入降雨数据后,模型的性能有了明显改善。在田纳西州坎伯兰河上进行的测试表明,在使用更多数据进行训练时,模型性能有望得到改善。
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CiteScore
2.60
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