Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Optimization of Row Pattern Matching over Sequence Data in Spark SQL Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Rainfall Estimation from Traffic Cameras

Authors : Remmy Zen, Dewa Made Sri Arsa, Ruixi Zhang, Ngurah Agus Sanjaya ER, Stéphane Bressan

Published in: Database and Expert Systems Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We propose and evaluate a method for the estimation of rainfall from images from a network of traffic cameras and rain gauges. The method trains a neural network for each camera under the supervision of the rain gauges and interpolates the results to estimate rainfall at any location. We study and evaluate variants of the method that exploit feature extraction and various interpolation methods. We empirically and comparatively demonstrate the superiority of a hybrid approach and of the inverse distance weighting interpolation for an existing comprehensive network of publicly accessible weather stations and traffic cameras.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Optimization of Row Pattern Matching over Sequence Data in Spark SQL
next chapter Towards Identifying De-anonymisation Risks in Distributed Health Data Silos
Footnotes
1
For this work, we neither consider the temporal sequences of measurements nor the sequence of images.
 
Literature
1.
Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th \(\{\)USENIX\(\}\) Symposium on Operating Systems Design and Implementation, \(\{\)OSDI\(\}\) 2016, pp. 265–283 (2016)
2.
Ahrens, B.: Distance in spatial interpolation of daily rain gauge data. Hydrol. Earth Syst. Sci. Discuss. 2(5), 1893–1922 (2005)MathSciNetCrossRef
3.
Akbari Asanjan, A., Yang, T., Hsu, K., Sorooshian, S., Lin, J., Peng, Q.: Short-term precipitation forecast based on the PERSIANN system and LSTM recurrent neural networks. J. Geophys. Res. Atmos. 123(22), 12–543 (2018)CrossRef
4.
Allamano, P., Croci, A., Laio, F.: Toward the camera rain gauge. Water Resour. Res. 51(3), 1744–1757 (2015)CrossRef
5.
Aswin, S., Geetha, P., Vinayakumar, R.: Deep learning models for the prediction of rainfall. In: 2018 International Conference on Communication and Signal Processing (ICCSP), pp. 0657–0661. IEEE (2018)
6.
Bargaoui, Z.K., Chebbi, A.: Comparison of two kriging interpolation methods applied to spatiotemporal rainfall. J. Hydrol. 365(1–2), 56–73 (2009)CrossRef
7.
Basistha, A., Arya, D., Goel, N.: Spatial distribution of rainfall in indian himalayas-a case study of Uttarakhand region. Water Res. Manag. 22(10), 1325–1346 (2008)CrossRef
8.
Cerqueira, R.F., Mantripragada, K.: Estimating rainfall precipitation amounts by applying computer vision in cameras, uS Patent App. 14/748,125, 26 May 2016
9.
Chen, F.W., Liu, C.W.: Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ, 10(3), 209–222 (2012)CrossRef
10.
Chen, Y.C., Wei, C., Yeh, H.C.: Rainfall network design using kriging and entropy. Hydrol. Process. Int. J. 22(3), 340–346 (2008)CrossRef
11.
12.
Cramer, S., Kampouridis, M., Freitas, A.A., Alexandridis, A.K.: An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives. Expert Syst. Appl. 85, 169–181 (2017)CrossRef
13.
Garg, K., Nayar, S.K.: Vision and rain. Int. J. Comput. Vis. 75(1), 3–27 (2007)CrossRef
14.
Haidar, A., Verma, B.: Monthly rainfall forecasting using one-dimensional deep convolutional neural network. IEEE Access 6, 69053–69063 (2018)CrossRef
15.
Hardwinarto, S., Aipassa, M., et al.: Rainfall monthly prediction based on artificial neural network: a case study in Tenggarong station, East Kalimantan-indonesia. Proc. Comput. Sci. 59, 142–151 (2015)CrossRef
16.
17.
Jiang, T.X., Huang, T.Z., Zhao, X.L., Deng, L.J., Wang, Y.: Fastderain: a novel video rain streak removal method using directional gradient priors. IEEE Transact. Image Process. 28(4), 2089–2102 (2019)MathSciNetCrossRef
18.
Kang, L.W., Lin, C.W., Fu, Y.H.: Automatic single-image-based rain streaks removal via image decomposition. IEEE Transact. Image Process. 21(4), 1742–1755 (2012)MathSciNetCrossRef
19.
20.
Kashiwao, T., Nakayama, K., Ando, S., Ikeda, K., Lee, M., Bahadori, A.: A neural network-based local rainfall prediction system using meteorological data on the internet: a case study using data from the Japan meteorological agency. Appl. Soft Comput. 56, 317–330 (2017)CrossRef
21.
Kim, S., Hong, S., Joh, M., Song, S.k.: Deeprain: ConvLSTM network for precipitation prediction using multichannel radar data. arXiv preprint arXiv:​1711.​02316 (2017)
22.
Kim, S., Kim, H.: A new metric of absolute percentage error for intermittent demand forecasts. Int. J. Forecast. 32(3), 669–679 (2016)CrossRef
23.
24.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105 (2012)
25.
Kurihata, H., et al.: Rainy weather recognition from in-vehicle camera images for driver assistance. In: IEEE Proceedings of Intelligent Vehicles Symposium, 2005, pp. 205–210. IEEE (2005)
26.
Kusiak, A., Wei, X., Verma, A.P., Roz, E.: Modeling and prediction of rainfall using radar reflectivity data: a data-mining approach. IEEE Transact. Geosci. Remote Sens. 51(4), 2337–2342 (2013)CrossRef
27.
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015)CrossRef
28.
Lee, I.J.: Big data processing framework of learning weather information and road traffic collision using distributed CEP from CCTV video: cognitive image processing. In: 2017 IEEE 16th International Conference on Cognitive Informatics and Cognitive Computing (ICCI* CC), pp. 400–406. IEEE (2017)
29.
Lee, J., Hong, B., Shin, Y., Jang, Y.J.: Extraction of weather information on road using CCTV video. In: 2016 International Conference on Big Data and Smart Computing (BigComp), pp. 529–531. IEEE (2016)
30.
Liu, J., Yang, W., Yang, S., Guo, Z.: D3r-Net: dynamic routing residue recurrent network for video rain removal. IEEE Transact. Image Process. 28(2), 699–712 (2019)MathSciNetCrossRef
31.
32.
Mair, A., Fares, A.: Comparison of rainfall interpolation methods in a mountainous region of a tropical island. J. Hydrol. Eng. 16(4), 371–383 (2010)CrossRef
33.
Meyer, H., Kühnlein, M., Appelhans, T., Nauss, T.: Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals. Atmos. Res. 169, 424–433 (2016)CrossRef
34.
Mu, P., Chen, J., Liu, R., Fan, X., Luo, Z.: Learning bilevel layer priors for single image rain streaks removal. IEEE Sig. Process. Lett. 26(2), 307–311 (2019)CrossRef
35.
Ramana, R.V., Krishna, B., Kumar, S., Pandey, N.: Monthly rainfall prediction using wavelet neural network analysis. Water Resour. Manage 27(10), 3697–3711 (2013)CrossRef
36.
Rani, B.K., Govardhan, A.: Rainfall prediction using data mining techniques-a survey. Comput. Sci. Inform. Technol. 3, 23–30 (2013)
37.
Roser, M., Moosmann, F.: Classification of weather situations on single color images. In: 2008 IEEE Intelligent Vehicles Symposium, pp. 798–803. IEEE (2008)
38.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference, pp. 517–524. ACM (1968)
39.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:​1409.​1556 (2014)
40.
Sirirattanapol, C., Nagai, M., Witayangkurn, A., Pravinvongvuth, S., Ekpanyapong, M.: Bangkok CCTV image through a road environment extraction system using multi-label convolutional neural network classification. ISPRS Int. J. Geo-Inf. 8(3), 128 (2019)CrossRef
41.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH
42.
43.
Stocker, T.F., et al.: Climate change 2013: The physical science basis (2013)
44.
Toledo-Moreo, R., et al.: Positioning and digital maps. In: Intelligent Vehicles, pp. 141–174. Elsevier (2018)
45.
Tripathi, A.K., Mukhopadhyay, S.: Removal of rain from videos: a review. Sig. Image Video Process. 8(8), 1421–1430 (2014)CrossRef
46.
Wu, J., Long, J., Liu, M.: Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148, 136–142 (2015)CrossRef
47.
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems, pp. 802–810 (2015)
48.
Yang, W., Tan, R.T., Feng, J., Liu, J., Yan, S., Guo, Z.: Joint rain detection and removal from a single image with contextualized deep networks. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (2019)
49.
Zhang, H., Patel, V.M.: Density-aware single image de-raining using a multi-stream dense network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 695–704 (2018)
Metadata
Title
Rainfall Estimation from Traffic Cameras
Authors
Remmy Zen
Dewa Made Sri Arsa
Ruixi Zhang
Ngurah Agus Sanjaya ER
Stéphane Bressan
Copyright Year
2019
Book
Database and Expert Systems Applications

Print ISBN: 978-3-030-27614-0

Electronic ISBN: 978-3-030-27615-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-27615-7_2

Premium Partner

    Image Credits