Skip to main content
SpringerLink
Log in

Missing Value Imputation for Wireless Sensory Soil Data: A Comparative Study

  • Conference paper
  • First Online:
Big Data Computing and Communications (BigCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9784))

Included in the following conference series:

  • 1554 Accesses

Abstract

Soil data is indispensable in hydrology or other environmental sciences. In present, the soil data is often collected by unattended wireless sensing system and then inevitably involves continuous missing values due to the unreliability of system, while in traditional commercial or manually-collected datasets, the data losses are sparsely distributed. time-series dataset, aimed at answering such a question: whether or not existing methods suit for wireless sensory soil dataset with continuous missing values, and how well they perform. With a real-world soil dataset involving two-month complete samples as the benchmark, we evaluate these six missing value infilling methods, compare their performance, and analyze the possible reasons behind. This study provides insights for designing new methods that can effectively deal with the missing values in wireless sensory soil dataset.

Jia Shao and Xingjian Ding are master candidates of computer science at Beijing Forestry University Information School. Guodong Sun is an associate professor of computer science at Beijing Forestry University Information School; and Hui Han is an assistant professor in Beijing Forestry University Information School.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Charoenhirunyingyosa, S., Hondaa, K., Kamthonkiatb, D., Inesc, A.: Soil moisture estimation from inverse modeling using multiple criteria functions. Comput. Electron. Agric. 75(2), 278–287 (2011)

    Article  Google Scholar 

  2. Coopersmith, E., Minsker, B., Wenzel, C., Gilmore, B.: Machine learning assessments of soil drying for agricultural planning. Comput. Electron. Agric. 104, 93–104 (2014)

    Article  Google Scholar 

  3. Coopersmitha, E., Minskera, B., Wenzelb, C., Gilmoreb, B.: Machine learning assessments of soil drying for agricultural planning. Comput. Electron. Agric. 104, 93–104 (2014)

    Article  Google Scholar 

  4. Culler, D., Estrin, D., Srivastava, M.: Overview of sensor networks. IEEE Comput. Mag. 37(8), 41–49 (2004)

    Article  Google Scholar 

  5. Dan, L., Sun, L., Dai, W.: Wireless sensor networks system of forest habitat factors collection. J. Harbin Inst. Technol. 46(7), 123–128 (2014)

    Google Scholar 

  6. Dumedah, G., Coulibaly, P.: Evaluation of statistical methods for infilling missing values in high-resolution soil moisture data. J. Hydrol. 400, 95–102 (2011)

    Article  Google Scholar 

  7. Dumedah, G., Walker, J., Chik, L.: Assessing artificial neural networks and statistical methods for infilling missing soil moisture records. J. Hydrol. 515(16), 330–344 (2014)

    Article  Google Scholar 

  8. Farhangfar, A., Kurgan, L., Dy, J.: Impact of imputation of missing values on classification error for discrete data. Pattern Recogn. 41, 3692–3705 (2008)

    Article  MATH  Google Scholar 

  9. Gong, J., Geng, J., Chen, Z.: Real-time gis data model and sensor web service platform for environmental data management. Int. J. Health Geographics 14(2) (2015)

    Google Scholar 

  10. Han, P., Wang, P., Zhang, S., Zhu, D.: Drought forecasting based on the remote sensing data using ARIMA models. Math. Comput. Model. 51(11–12), 1398–1403 (2010)

    Article  Google Scholar 

  11. Hardy, A., Barr, S., Mills, J., Miller, P.: Characterising soil moisture in transport corridor environments using airborne LIDAR and CASI data. Hydrol. Process. 26(13), 1925–1936 (2012)

    Article  Google Scholar 

  12. Kohn, R., Ansley, C.: Estimation, prediction, and interpolation for arima models with missing data. J. Am. Stat. Assoc. 81(395), 751–761 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  13. Kornelsen, K., Coulibaly, P.: Comparison of interpolation, statistical, and data-driven methods for imputation of missing values in a distributed soil moisture dataset. J. Hydrol. Eng. 19(1), 26–43 (2014)

    Article  Google Scholar 

  14. Lee, W., Alchanatis, V., Yang, C., Hirafuji, M., Moshou, D., Li, C.: Sensing technologies for precision specialty crop production. Comput. Electron. Agric. 74(1), 2–33 (2010)

    Article  Google Scholar 

  15. Li, J., Gao, H.: Survey on sensor network research. J. Softw. 45(1), 1–15 (2008). (in Chinese)

    Google Scholar 

  16. Lindenmayer, D., Likens, G.: The science and application of ecological monitoring. Biol. Conserv. 143, 1317–1328 (2010)

    Article  Google Scholar 

  17. Meijering, E.: A chronology of interpolation: from ancient astronomy to modern signal and image processing. Proc. IEEE 90, 319–342 (2002)

    Article  Google Scholar 

  18. Moorthy, K., Mohamad, M.S., Deris, S.: A review on missing value imputation algorithms for microarray gene expression data. Current Bioinform. 9, 18–22 (2014)

    Article  Google Scholar 

  19. Mukhopadhyay, S., Jiang, J. (eds.): Wireless Sensor Networks and Ecological Monitoring (Smart Sensors, Measurement and Instrumentation). Springer, Heidelberg (2013)

    Google Scholar 

  20. Nemes, A., Wosten, J., Varallyay, G., Bouma, J.: Soil water balance scenariostudies using predicted soil hydraulic parameters. Hydrol. Process. 20(5), 1075–1094 (2006)

    Article  Google Scholar 

  21. Ojha, T., Misraa, S., Raghuwanshib, N.: Wireless sensor networks for agriculture: the state-of-the-art in practice and future challenges. Comput. Electron. Agric. 118, 66–84 (2015)

    Article  Google Scholar 

  22. Pigott, T.: A review of methods for missing data. Educ. Res. Eval. Int. J. Theory Pract. 7, 353–383 (2001)

    Article  Google Scholar 

  23. Pomati, F., Jokela, J., Simora, M., Veronesi, M., Ibelings, B.: An automated platform for phytoplankton ecology and aquatic ecosystem monitoring. Environ. Sci. Technol. 45(22), 9658–9665 (2011)

    Article  Google Scholar 

  24. Schneider, A.: Monitoring land cover change in urban and peri-urban areas using dense time stacks of landsat satellite data and a data mining approach. Remote Sens. Environ. 124, 689–704 (2012)

    Article  Google Scholar 

  25. Sun, G., Xu, B.: Drag: a priority-guaranteed routing for sensor network with low duty-cycles. Ad Hoc Sens. Wirel. Netw. 13(1–2), 39–58 (2011)

    MathSciNet  Google Scholar 

  26. Vachaud, G., Silans, A.P.D., Balabanis, P., Vauclin, M.: Temporal stability of spatially measured soil water probability density function. Soil Sci. Soc. Am. J. 49(49), 822–828 (1985)

    Article  Google Scholar 

  27. Wang, G., Garciab, D., Liu, Y., Jeua, R., Dolmana, A.: A three-dimensional gap filling method for large geophysical datasets: application to global satellite soil moisture observations. Environ. Model. Softw. 30, 139–142 (2012)

    Article  Google Scholar 

  28. Wang, J., Damevski, K., Chen, H.: Sensor data modeling and validating for wireless soil sensor network. Comput. Electron. Agric. 112, 75–82 (2015)

    Article  Google Scholar 

  29. Wang, N., Zhang, N., Wang, M.: Wireless sensors in agriculture and food industryrecent development and future perspective. Comput. Electron. Agric. 50(1), 1–14 (2006)

    Article  Google Scholar 

  30. Yang, J., Zhang, C., Li, X.: Integration of wireless sensor networks in environmental monitoring cyber infrastructure. Wirel. Netw. 16(4), 1091–1108 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported, in part, by the NSF of China with Grant no. 61300180 and the Fundamental Research Funds for the Central Universities of China with Grant no. TD2014-01.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Beijing Forestry University, Beijing, 100083, China

    Guodong Sun, Jia Shao, Hui Han & Xingjian Ding

Authors
  1. Guodong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jia Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hui Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingjian Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guodong Sun .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of N. Carolina at Charlotte, Charlotte, North Carolina, USA

    Yu Wang

  2. Northeastern University, College of Information Science and Engineering, Shenyang, Liaoning, China

    Ge Yu

  3. Department of Electrical & Computer Engineering, Rutgers University, Piscataway, New Jersey, USA

    Yanyong Zhang

  4. Department of Electrical and Computer Engineering, University of Houston Department of Engineering, Houston, Texas, USA

    Zhu Han

  5. College of Information Science and Engineering, Northeastern University, Shenyang , Liaoning, China

    Guoren Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Sun, G., Shao, J., Han, H., Ding, X. (2016). Missing Value Imputation for Wireless Sensory Soil Data: A Comparative Study. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42553-5_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42552-8

  • Online ISBN: 978-3-319-42553-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation