Rainfall Run Off Modeling Using Fuzzy Numbers
DOI:
https://doi.org/10.51983/tarce-2016.5.1.2226Keywords:
Rainfall, runoff, modelingAbstract
This paper proposes a new approach to predict the flow characteristic of the ungauged watershed. The selected study area has only one rain gauge station located near the Kundha bridge giving birth to considerable uncertainties in predicting the catchment flow. Application of fuzzy logic in prediction can minimize these uncertainties of the ungauged watershed. Fuzzy logic model helps to simulate the unknown relationship between the hydrological data and the catchment attributes. The research paper weighs soil type, land use, slope with fuzzy numbers. Finally, to obtain the runoff the comprehensive weight is multiplied with the rainfall data. The runoff thus obtained is used to create runoff map of the watershed. Natural break classification system is utilized to classify the runoff of the entire watershed. The model is validated with the water level records of the Kundha reservoir. This runoff map is valuable in landslide susceptibility analysis. This model not only serves for prediction but also prove to be an effective tool for further research.
References
Abrahart, R.J. and See, L. (2000). Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrological Processes, 14, pp. 2157-2172.
Band, L.E., 1993. Effect of land surface representation on forest water and carbon budgets. Journal of Hydrology 150: 749-772
Bjerklie, D. M.: Estimating the bankfull velocity and discharge for rivers using remotely sensed river morphology information, J. Hydrol., 341, 144–155, 2007.
Box, G.E.P. and Jenkins, G.M. (1970). Time series analysis. Holden-Day, San Francisco, CA, USA.
Cirmo, C.P. and McDonnell, J.J., 1997. Linking the hydrologic and biochemical controls of nitrogen transport in near-stream zones of temperate-forested catchments: a review. Journal of Hydrology 199: 88-120
Dilip Kumar, Rajib Kumar Bhattacharjya “Distributed Rainfall Runoff Modeling”, International Journal of Earth Sciences and Engineering, Volume 04, No 06 SPL, October 2011, pp. 270-275.
Evans, R., 1996. Some soil factors influencing accelerated water erosion of arable land (progress report). Progress in Physical Geography 20(2): 205-215
Kouraev, A. V., Zakharova, E. A., Samain, O., Mognard, N. M., and Cazenave, A.: Ob’s river discharge from OPEX/Poseidon satellite altimetry (1992–2002), Remote Sens. Environ., 93, 238– 245, 2004.
Moore, I.D., Norton, T.W. and Williams, J.E., 1993. Modelling environmental heterogeneity in forested landscapes. Journal of Hydrology 150: 717-747.
Price, R.K. (2006). The growth and significance of hydroinformatics. In: D.W. Knight and A.Y. Shamseldin (eds.), River Basin Modelling for Flood Risk Mitigation, Taylor & Francis, London, UK.
Sherman, L. (1932). Streamflow from rainfall by the unit-graph method. Engineering News Record, 108, pp. 501-505.
Solomatine, D., Maskey, M. and Shrestha, D.L. (2006). Eager and lazy learning methods in the context of hydrologic forecasting, Proc. of International Joint Conference on Neural Networks, Vancouver, BC, Canada, IEEE. [13] Tokar AS, Markus M. 2000. Precipitation–runoff modeling using artificial neural network and conceptual models. Journal of Hydrologic Engineering, American Society of Civil Engineers 5(2): 156–161.
Wei-dong, W., Cui-ming, X. and Xiang-gang, D. “Landslides susceptibility mapping in Guizhou province based on fuzzy theory”, Mining Science and Technology, Vol. 19, pp. 399 - 404, 2009.
J. G¨otzinger and A. B´ardossy , Integration and calibration of a conceptual rainfall-runoff model in the framework of a decision support system for river basin management, Advances in Geosciences, 5, 31–35, 2005.
Binaghi, E., Luzi, L., Madella, P., Pergalan, F. and Rampini, A. “Slope instability zonation: A comparison between certainty factor and fuzzy Dempster–Shafer approaches”, Natural Hazards, Vol. 17, pp. 77 - 97, 1998.
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