A review of video‐based rainfall measurement methods

Abstract

Accurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video‐based rainfall measurement, a novel method, has the advantages of real‐time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video‐based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video‐based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning‐based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video‐based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks.This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes

A Review of Non-Contact Water Level Measurement Based on Computer Vision and Radar Technology

“…With high-density, low-cost groundbased video networks, hydrological monitoring using computer vision algorithms becomes an attractive solution to enhance the spatial and temporal representation of existing monitoring instruments. The use of computer vision for hydrological monitoring including rainfall monitoring [20][21][22], water level monitoring [23,24] and surface flow velocity measurement [25][26][27] has become a popular paradigm. The computer visual-based paradigm for water level measurement presents challenges in two aspects: accurate water line detection and water level elevation calculation.…”