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Reconstruction of Missing Hourly Precipitation Data to Increase Training Data Set for ANN

Author

Listed:
  • Hema Nagaraja

    (Department of Computer Science, Jaypee Institute of Information Technology, Noida, India)

  • Krishna Kant

    (Manav Rachna International University, Faridabad, India)

  • K. Rajalakshmi

    (Jaypee Institute of Information Technology, Noida, India)

Abstract

This paper investigates the hourly precipitation estimation capacities of ANN using raw data and reconstructed data using proposed Precipitation Sliding Window Period (PSWP) method. The precipitation data from 11 Automatic Weather Station (AWS) of Delhi has been obtained from Jan 2015 to Feb 2016. The proposed PSWP method uses both time and space dimension to fill the missing precipitation values. Hourly precipitation follows patterns in particular period along with its neighbor stations. Based on these patterns of precipitation, Local Cluster Sliding Window Period (LCSWP) and Global Cluster Sliding Window Period (GCSWP) are defined for single AWS and all AWSs respectively. Further, GCSWP period is classified into four different categories to fill the missing precipitation data based on patterns followed in it. The experimental results indicate that ANN trained with reconstructed data has better estimation results than the ANN trained with raw data. The average RMSE for ANN trained with raw data is 0.44 and while that for neural network trained with reconstructed data is 0.34.

Suggested Citation

  • Hema Nagaraja & Krishna Kant & K. Rajalakshmi, 2018. "Reconstruction of Missing Hourly Precipitation Data to Increase Training Data Set for ANN," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 9(1), pages 62-84, January.
    • Handle: RePEc:igg:jaeis0:v:9:y:2018:i:1:p:62-84
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