Recently, surveillance technology was proposed as an alternative to flood monitoring systems. This study introduces a novel approach to flood monitoring by integrating surveillance technology and LiDAR data to estimate river water levels. The methodology involves deep learning semantic segmentation for water extent extraction before utilizing the segmented images and virtual markers with elevation information from light detection and ranging (LiDAR) data for water level estimation. The efficiency was assessed using Spearman's rank-order correlation coefficient, yielding a high correlation of 0.92 between the water level framework with readings from the sensors. The performance metrics were also carried out by comparing both measurements. The results imply accurate and precise model predictions, indicating that the model performs well in closely matching observed values. Additionally, the semi-automated procedure allows data recording in an Excel file, offering an alternative measure when traditional water level measurement is not available. The proposed method proves valuable for on-site water-related information retrieval during flood events, empowering authorities to make informed decisions in flood-related planning and management, thereby enhancing the flood monitoring system in Malaysia.

最近，有人提出监视技术作为洪水监测系统的替代方案。本研究引入了一种新的洪水监测方法，通过集成监测技术和激光雷达数据来估计河流水位。该方法涉及深度学习语义分割来提取水域范围，然后利用分割图像和虚拟标记以及来自光探测和测距（LiDAR）数据的海拔信息来估计水位。使用 Spearman 的排序相关系数评估效率，水位框架与传感器读数之间的相关性高达 0.92。性能指标也是通过比较这两种测量值来进行的。结果意味着模型预测准确且精确，表明该模型在紧密匹配观测值方面表现良好。此外，半自动程序允许将数据记录在 Excel 文件中，在传统水位测量不可用时提供替代措施。事实证明，所提出的方法对于洪水事件期间的现场水相关信息检索很有价值，使当局能够在洪水相关规划和管理中做出明智的决策，从而增强马来西亚的洪水监测系统。