Ibrar Muhammad Ahsan, Usama Muhammad, Salman Abdullahi M
Department of Civil & Environmental Engineering, The University of Alabama in Huntsville, 35899, Huntsville, AL, USA.
Department of Civil & Environmental Engineering, Northeastern University, 02115, Boston, MA, USA.
Sci Rep. 2025 Feb 12;15(1):5230. doi: 10.1038/s41598-025-89196-6.
Tropical cyclones have a significant impact on estuaries, resulting in deteriorating water quality, changes in phytoplankton productivity, as well as scouring and physical harm to mangroves and other vegetation. Furthermore, they have the potential to impede, pause, or even reverse ecosystem restoration and management efforts. As such, it is crucial to devise methods for detecting and measuring the severity of estuarine disturbances caused by tropical cyclones. Several statistical methods have been proposed in the past to detect and quantify disturbances, with varying levels of complexity and accuracy. Recent advancements in data collection and the quality of high-frequency data have opened up the opportunity to employ machine learning models for assessing the impact of tropical cyclones on ecosystems. This study develops a new machine learning-based model for detecting disturbances within estuaries, assessing their severity, and determining the recovery time, primarily focusing on disruptions to estuarine water quality. A Long Short-Term Memory (LSTM)-based deep learning model is developed to detect disturbances and quantify their severity while a Gaussian filter-based algorithm is developed to assess recovery time. The research utilizes data from NOAA's National Estuarine Research Reserve System for training and validating the model. The model demonstrates an ability to distinguish between disturbances in water quality caused by tropical cyclones and those resulting from natural variability. It also assesses the extent of the disruption and the time for the estuary to revert to its pre-event state. Detecting and quantifying disturbances, as well as estimating recovery time in estuaries due to tropical cyclones, represent the initial steps in the development of predictive disturbance models. Detecting and quantifying disturbance can also aid stakeholders in comprehending the severity of tropical cyclones' impacts on aquatic systems, allowing for the development of suitable interventions. The developed model can be applied to detect and quantify anomalies in any time series data, making it useful in various other fields.
热带气旋对河口有重大影响，导致水质恶化、浮游植物生产力变化，以及对红树林和其他植被的冲刷和物理损害。此外，它们有可能阻碍、暂停甚至逆转生态系统恢复和管理工作。因此，设计检测和测量热带气旋引起的河口扰动严重程度的方法至关重要。过去已经提出了几种统计方法来检测和量化扰动，其复杂程度和准确性各不相同。数据收集的最新进展和高频数据的质量为采用机器学习模型评估热带气旋对生态系统的影响提供了机会。本研究开发了一种新的基于机器学习的模型，用于检测河口内的扰动、评估其严重程度并确定恢复时间，主要关注河口水质的破坏。开发了一种基于长短期记忆（LSTM）的深度学习模型来检测扰动并量化其严重程度，同时开发了一种基于高斯滤波器的算法来评估恢复时间。该研究利用美国国家海洋和大气管理局国家河口研究保护区系统的数据来训练和验证模型。该模型展示了区分热带气旋引起的水质扰动和自然变化引起的扰动的能力。它还评估了破坏的程度以及河口恢复到事件前状态的时间。检测和量化扰动，以及估计热带气旋导致的河口恢复时间，是预测性扰动模型开发的初步步骤。检测和量化扰动还可以帮助利益相关者理解热带气旋对水生系统影响的严重程度，从而制定合适 的干预措施。所开发的模型可应用于检测和量化任何时间序列数据中的异常，使其在其他各个领域都有用处。
