Rainfall estimation and rain gauge comparison for x-band polarimetric CASA radars

Trabal, Jorge M.; McLaughlin, D.J.

doi:10.1109/igarss.2007.4423406

Cited by 8 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, since F1 = M × N is the first extracted frame and F2 = M × N the second extracted frame, the first differential image is given by (1).…”

Section: Video Learning Datasetmentioning

confidence: 99%

See 1 more Smart Citation

A CNN-based Differential Image Processing Approach for Rainfall Classification

Avanzato¹,

Beritelli²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

With the aim of preventing hydro-geological risks and overcoming the problems of current rain gauges, this paper proposes a low-complexity and cost-effective video rain gauge. In particular, in this paper the authors propose a new approach to rainfall classification based on image processing and video matching process employing convolutional neural networks (CNN). The system consists of a plastic shaker, a video camera and a low cost, low power signal processing unit. The use of differential images allows for greater robustness, guaranteeing full background subtraction. As regards precision, speed and ability to adapt to variations in precipitation intensity, the proposed method achieves good performance. In particular, the results obtained from seven classes, ranging from "No rain" to "Cloudburst", applying the Discrete Cosine Transform (DCT) to the differential images on 16x16 sub-blocks show an average accuracy of 75% considering, also, the adjacent missclassification. Furthermore, the analysis of precision and sensitivity parameters yields excellent results. The proposed method is very innovative, in fact, the few studies found in the state of the art use only two classification classes (No rain and Rain), while our method contains seven classification classes and overall delivers very good accuracy performance.

show abstract

“…Therefore, since F1 = M × N is the first extracted frame and F2 = M × N the second extracted frame, the first differential image is given by (1).…”

Section: Video Learning Datasetmentioning

confidence: 99%

“…People have realized, however, that these instruments do not perform well in terms of temporal precision, classification accuracy and territorial coverage. Over the years, different approaches have been studied to tackle this issue, such as the use of radars and satellites [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

A CNN-based Differential Image Processing Approach for Rainfall Classification

Avanzato¹,

Beritelli²

2020

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…The sudden climate change, which has taken place in recent years, has generated calamitous phenomena linked to hydrogeological instability in many areas of the world. The main existing rainfall level measurement methods employ rain gauges, weather radars and satellites [1][2][3]. The rain gauge is probably the most common rainfall measurement device, as it is able to provide an average accurate estimate of the rainfall with a precise temporal resolution; in fact, rain gauges continuously record the level of precipitation even within short time intervals.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Acoustic Rain Gauge Based on Convolutional Neural Networks

Avanzato

Beritelli

2020

Information

View full text Add to dashboard Cite

An accurate estimate of rainfall levels is fundamental in numerous application scenarios: weather forecasting, climate models, design of hydraulic structures, precision agriculture, etc. An accurate estimate becomes essential to be able to warn of the imminent occurrence of a calamitous event and reduce the risk to human beings. Unfortunately, to date, traditional techniques for estimating rainfall levels present numerous critical issues. The algorithm applies the Convolution Neural Network (CNN) directly to the audio signal, using 3 s sliding windows with an offset of only 100 milliseconds. Therefore, by using low cost and low power hardware, the proposed algorithm allows implementing critical high rainfall event alerting mechanisms with short response times and low estimation errors. More specifically, this paper proposes a new approach to rainfall estimation based on the classification of different acoustic timbres that rain produces at different intensities and on CNN. The results obtained on seven classes ranging from “No rain” to “Cloudburst” indicate an average accuracy of 75%, which rises to 93% if the misclassifications of the adjacent classes are not considered. Some application contexts concern smart cities for which the integration of an audio sensor inside the luminaire of a street lamp is foreseen, precision agriculture, as well as highway safety, by minimizing the risks of aquaplaning.

show abstract

“…• weather radars have the advantage of being able to monitor a larger area, compared to the rain gauge, and to determine the real distribution of rainfall [9], [10], but they are very expensive;…”

Section: Introductionmentioning

confidence: 99%