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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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NAAS Score: *5.38 (2020)
[Effective from January 1, 2020]
For more details click here

ICV 2018: 95.39
Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
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Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2017.6(10): 2736-2748
DOI: https://doi.org/10.20546/ijcmas.2017.610.322


Development of Fuzzy Logic Based Rainfall-Runoff Model for Kelo River Macro Watershed of Mahanadi Basin
Gayam Sujatha1, Jitendra Sinha1*, Nilima Jangre1 and Ashish Patel2
1Department of Soil and Water Engineering, FAE, IGKV, Raipur-492006, Chhattisgarh, India
2National Institute of Technology, Raipur-492006, Chhattisgarh, India
*Corresponding author
Abstract:

A rainfall – runoff model describes the relation between the rainfall and runoff for a particular catchment area. The relationship between rainfall in a period and the corresponding runoff is quite complex. SCS-CN method is used to estimate the runoff, but, SCS-CN method does not consider the impact of rainfall intensity and did not account for the influence of forest management practices. The aim of this study is to develop Fuzzy Logic Model to estimate runoff using rainfall given for the area under consideration. The area considered for this study is Kelo macro-watershed of Mahanadi basin, Raigarh, Chhattisgarh. The daily rainfall and gauge – discharge data for past 12 years (2002 to 2013) were used. Weighted rainfall for the study area was estimated by constructing the Thiessen polygons. More than 90% of the rain falls in the active period (AP) of months 1st July – 31st October. The catchment behaviour to infiltration and other losses was found to be variable with the average runoff-rainfall ratio of 0.44. Out of 12 years, 9 years data was used for calibration/training of the model while remaining 3 years data was used for model verification. Fuzzy Logic Model developed with nine numbers of linguistic variables as extremely low (EL), very very low (VVL), very low (VL), low (L), medium (M), moderately high (MH), high (H), very high (VH) and extremely high (EH). The most common triangular and trapezoidal membership functions have been adopted for each input and output. The model operates on an ‘if-then’ principle, where the ‘if’ is a vector of fuzzy premises and the ‘then’ is a vector of fuzzy consequences. The developed FL model and SCS-CN method has been analyzed on basis of various performance indices, i.e., Mean Absolute Deviation (MAD), Root Mean Square Error (RMSE), Coefficient of Correlation (CC), and Coefficient of Efficiency (CE). Runoff has been estimated through developed fuzzy logic model, SCS- Curve Number and has been compared with multiyear observed flow. On comparison of SCS-CN and FL, it is visible that the MAD and RMSE values are 2.46 mm and 4.14 mm for SCS-CN method and 2.06 mm and 3.45 mm for fuzzy logic during training respectively. Similarly, it is 2.42 mm and 4.4 mm for SCS-CN method and 2.29 mm and 3.62 mm for fuzzy logic during testing respectively. Again comparison of Coefficient of Correlation (CC) and Coefficient of Efficiency (CE) values are 93.74 % and 83.99 % for SCS-CN method and 95.78 % and 88.88 % for FL during training respectively. Similarly, it is 92.03 % and 81.32 % for SCS-CN method and 94.99 % and 87.33 % for FL during testing respectively. So from MAD, RMSE, CC and CE point of view, FL is performing better than SCS-CN method. Scatter plots between observed and simulated SCS-CN values showed that the most of the values lie near 45° line and it is clear from the plots that the model is underestimating the higher values in training while slightly overestimating the lower values in testing period. Scatter plots between observed and predicted FL values showed that most of the values lie near 45° line and it is clear from the plots that the model is underestimating the higher values in training while lower values are almost perfectly matched with the observed runoff values in testing period. FL Model is very well describing runoff as compared to SCS-CN method for the study area. Thus, it can be concluded that use of FL models are certainly a much better choice than the SCS-CN method for rainfall – runoff modeling of the study area.


Keywords: SCS-Curve Number, Runoff, Fuzzy logic, Membership function, IF-THEN rules, Training, Testing.
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How to cite this article:

Gayam Sujatha, Jitendra Sinha, Nilima Jangre and Ashish Patel. 2017. Development of Fuzzy Logic Based Rainfall-Runoff Model for Kelo River Macro Watershed of Mahanadi Basin.Int.J.Curr.Microbiol.App.Sci. 6(10): 2736-2748. doi: https://doi.org/10.20546/ijcmas.2017.610.322