Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Yin, Hang; Zheng, Feifei; Duan, Huan‐Feng; Savić, Dragan; Kapelan, Zoran

doi:10.1016/j.eng.2021.11.021

Cited by 23 publications

(28 citation statements)

References 44 publications

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“…To minimize estimation uncertainties, each evaluation case was repeated five times, and Table 3 reports the average performance obtained over the five runs. Note that the irCNN model performance reported in Table 3 differs from that presented in Yin et al (2023) which used only daytime rainfall images, whereas the results reported also include nighttime images.…”

Section: Evaluation Of Results By Random Samplingmentioning

confidence: 91%

“…However, at night, the extraction algorithm does not have to contend with ambient environmental brightness. The net effect is that, although overall performance declines at night, the two-stage method achieves better overall performance than the original (Yin et al, 2023) methodology.…”

Section: Evaluation Of Results By Random Samplingmentioning

confidence: 99%

“…After extracting raindrop information from the rainfall images, we use it as input data when training the irCNN machine learning model (Yin et al, 2023) to estimate rainfall intensity. This works, because rainfall intensity is strongly related to the density and size of raindrops in the image, expressed mathematically as:…”

Section: Rain Intensity Estimationmentioning

confidence: 99%

“…For example, Yin et al. (2023) trained a Convolutional Neural Network (CNN), named the irCNN, to estimate rainfall from daytime rainfall imagery. However, reported limitations of that approach include: (a) the need for large amounts of training data, and (b) poor generalization ability to different conditions (and especially nighttime).…”

Section: Introductionmentioning

confidence: 99%

“…The idea is, that rainfall images captured by surveillance cameras can be processed through machine learning algorithms in order to estimate rainfall intensities. For example, Yin et al (2023) trained a Convolutional Neural Network (CNN), named the irCNN, to estimate rainfall from daytime rainfall imagery. However, reported limitations of that approach include: (a) the need for large amounts of training data, and (b) poor generalization ability to different conditions (and especially nighttime).…”

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confidence: 99%

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Toward Improved Real‐Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Zheng

Yin

et al. 2023

Water Resources Research

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Under global climate change, urban flooding occurs frequently, leading to huge economic losses and human casualties. Extreme rainfall is one of the direct and key causes of urban flooding, and accurate rainfall estimates at high spatiotemporal resolution are of great significance for real‐time urban flood forecasting. Using existing rainfall intensity measurement technologies, including ground rainfall gauges, ground‐based radar, and satellite remote sensing, it is challenging to obtain estimates of the desired quality and resolution. However, an approach based on processing distributed surveillance camera network imagery through machine learning algorithms to estimate rainfall intensities shows considerable promise. Here, we present a novel approach that first extracts raindrop information from the surveillance camera images (rather than using the raw imagery directly), followed by the use of convolutional neural networks (CNN) to estimate rainfall intensity from the resulting raindrop information. Evaluation of the approach on 12 rainfall events under both daytime and nighttime conditions shows that generalization ability, and especially nighttime predictive performance, is significantly improved. This represents an important step toward achieving real‐time, high spatiotemporal resolution, measurement of urban rainfall at relatively low cost.This article is protected by copyright. All rights reserved.

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Section: Evaluation Of Results By Random Samplingmentioning

confidence: 91%

Section: Evaluation Of Results By Random Samplingmentioning

confidence: 99%

Section: Rain Intensity Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Toward Improved Real‐Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Zheng

Yin

et al. 2023

Water Resources Research

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Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform‐based machine learning models

Küllahcı,

Altunkaynak

2024

Intl Journal of Climatology

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Rainfall is an important phenomenon for various aspects of human life and the environment. Accurate prediction of rainfall is crucial for a wide range of sectors, including agriculture, water resources management, energy production, disaster management and many more. The ability to predict rainfall in an accurate fashion enables stakeholders to make informed decisions and take necessary actions to mitigate the impacts of natural disasters, water scarcity and other issues related to rainfall. In addition, advances in rainfall prediction technologies have the potential to contribute to sustainable water management and the preservation of water resources by providing the necessary information for decision‐makers to plan and implement effective water management strategies. Hence, it is important to continuously improve the accuracy of rainfall prediction. In this paper, the integration of the maximum overlap discrete wavelet transform (MODWT) and machine learning algorithms for daily rainfall prediction is proposed. The main objective of this study is to investigate the potential of combining MODWT with various machine‐learning algorithms to increase the accuracy of rainfall prediction and extend the forecast time horizon to 3 days. In addition, the performances of the proposed hybrid models are contrasted with the models hybridized with commonly used discrete wavelet transform (DWT) algorithms in the literature. For this, daily rainfall raw data from three rainfall observation stations located in Turkey are used. The results show that the proposed hybrid MODWT models can effectively improve the accuracy of precipitation forecasting, based on model evaluation measures such as mean square error (MSE) and Nash‐Sutcliffe coefficient of efficiency (CE). Accordingly, it can be concluded that the integration of MODWT and machine learning algorithms have the potential to revolutionize the field of daily rainfall prediction.

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A review of video‐based rainfall measurement methods

Yan

Chen

et al. 2023

WIREs Water

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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

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Estimating Rainfall Intensity Using an Image-Based Deep Learning Model

Cited by 23 publications

References 44 publications

Toward Improved Real‐Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Toward Improved Real‐Time Rainfall Intensity Estimation Using Video Surveillance Cameras

Maximizing daily rainfall prediction accuracy with maximum overlap discrete wavelet transform‐based machine learning models

A review of video‐based rainfall measurement methods

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