Follow
International Journal of Current Microbiology and Applied Sciences (IJCMAS)
IJCMAS is now DOI (CrossRef) registered Research Journal. The DOIs are assigned to all published IJCMAS Articles.
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00 For more details click here
National Academy of Agricultural Sciences (NAAS) : NAAS Score: *5.38 (2019) [Effective from January 1, 2019]For more details click here

Login as a Reviewer

Indexed in



National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

See Guidelines to Authors
Current Issues

Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(1): 1564-1572
DOI: https://doi.org/10.20546/ijcmas.2019.801.164


Application of Standard Models and Artificial Neural Network for Missing Rainfall Estimation
Madhuri Dubey1* and M.K. Hardaha2
1Indian Institute of Technology, Kharagpur, West Bengal, India
2College of Agricultural Engineering, J.N.K.V.V., Jabalpur, Madhya Pradesh, India
*Corresponding author
Abstract:

Precipitation records often suffer from missing data values for certain time periods due to various reasons, one of them being the malfunctioning of rain gauges. This is an important issue in practical hydrology as it affects the continuity of rainfall data. The missing data values ultimately influence the results of hydrologic studies that use rainfall data as one of the input variables. Therefore, it is crucial to estimate the missing rainfall data for qualitative hydrologic assessment. In this study, the annual rainfall data of eight districts of the state Madhya Pradesh, India is collected in the time frame of the year 1901 to 2011. The collected information is used for estimating missing annual rainfall data. Various existing standard models, such as arithmetic mean, normal ratio, inverse distance weighting, multiple linear regression as well as unconventional methods like artificial neural network (ANN) is used and compared to determine missing rainfall records in the collected data. The results as obtained show that among the various standard models, multiple linear regression models perform better. The model is validated and the correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) are found to be 0.913, 9017 mm, and 49.7 mm, respectively. When the ANN model is applied for estimating annual missing rainfall data, it is found that Levenverg Marquardt (lm) algorithm with 7 neurons and 50-year length of records performs better than the other combination of algorithms, neurons and length of records. During the training of this model, the values of R, RMSE and MAE value are found to be 0.998, 4.4x10-4 mm and 53.047 mm, respectively, and during validation, they are 0.858, 1.667 mm and 49.103 mm, respectively. The results as obtained indicate that the ANN method is most suitable for estimating the missing annual rainfall data.


Keywords: Arithmetic mean model, Normal ratio model, Inverse distance model, Artificial neural network
Download this article as Download

How to cite this article:

Madhuri Dubey and Hardaha, M.K. 2019. Application of Standard Models and Artificial Neural Network for Missing Rainfall Estimation.Int.J.Curr.Microbiol.App.Sci. 8(1): 1564-1572. doi: https://doi.org/10.20546/ijcmas.2019.801.164