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Rainfall - Runoff Modeling Using Artificial Neural Network Of Perumal Tank, Cuddalore District, Tamil Nadu, India
Subject area: Science,Engineering and Technology · Area of research: Civil Engineering
Abstract
Rainfall-runoff models are highly useful for water resources planning, development and flood mitigation. Rainfall-runoff analysis is quite difficult due to presence of complex nonlinear relationship in the transformation of rainfall to run-off however runoff analysis is very important for the predication of natural calamities like floods and drought. A rainfall-runoff model is a mathematical model describing the rainfall-runoff relations of a catchment area, drainage basin or washed. In the model calculates the rainfall into runoff. In rainfall-runoff modelling SCS-CN (Soil Conservation Service ? Curve Number) method uses the soil information, rainfall, storm duration, soil texture, type & extent of vegetative cover and conservation practices are considered. A new dimension has been added to the modelling approach through the adoption of the ANN (Artificial Neural Network) technique as these models possess desirable attributes of universal approximation, and the ability to learn from examples. The ANN is well known as a flexible mathematical tool and has the ability to generalize patterns in precise and ambiguous input and output data sets without attempting to reach understanding as to the nature of the phenomena. In the present study a feed forward back propagation algorithm of ANN model is used for Perumal tank, Uppanar sub basin in Kurinjipadi Taluk of Cuddalore District.
Keywords
Rainfall, Runoff, Soil Conservation Service, Curve Number, Artificial Neural Network
References
[1] Viji. R., Rajesh Prasanna. P., Ilangovan. R." Gis Based SCS - CN Method For Estimating Runoff In Kundahpalam Watershed, Nilgries District, Tamilnadu", Earth Science Research Journal.Volume:19 no:1 Bogota January June 2015. (9/22/2016).
[2] Ghumman. A. R., Yousry Ghazaw. M., Sohail. A. R., Watanabe. K."Runoff forecasting by artificial neural network and conventional model", December 2011 Pages 345-350. (9/18/2016).
[3] Santosh Patil. K., Dr Shrinivas Valunjkar. S."Prediction of Daily Runoff using Time Series Forecasting and ann Models", pages 241-245.(9/18/2016).
[4] Valunjkar. S. S., Santosh Patil., Alka Kote. "Forecasting of Daily Runoff by Ann for Gunjvani Watershed" 4-6, December 2013.(9/18/2016).
[5] Sreenivasa Rao. G., Giridhar. M. V. S. S." Daily Runoff Forecasting using Artificial Neural Network" International Journal of Scientific & Engineering Research, Volume 7, June 2016.Pages 478- 483.
[6] Pasupati Shrestha. M., Dr (Mrs) Geetha Jayaraj. K. "Review - Hydrological forecasting by using SCS-CN Modelling", International Journal on Recent and Innovation Trends in Computing and Communication, Volume 4.Pages 177-181.
[7] Vinithra. R., Yeshodha. L. "Rainfall - Runoff Modelling Using SCS -CN Method: A Case Study of Krishnagiri District, Tamilnadu", Volume 5 Issue 3, March 2016.Pages 2080-2084.
[8] Vaishali Bhuktar. S., Dr Regulwar. D. G. "Computation of Runoff by SCS-CN Method and GIS" Volume 01, No.6, June 2015.Pages 63-69.
[9] Kulkarni Nilesh .K., Shete. V. V. "ANN Based Hydraulic Modeler for Flood Prediction", Volume -2 Issue -3, 2014.
[10] Mr. Mahesh Shrivastav. B., Haresh Gandhi. M., Pinak Ramanuj. S., Milan Chudasama. K., Mr. Jignesh Joshi. A. "Prediction of Runoff using Artificial Networks (A Case Study of Khodiyar Catchment Area)",International Journal of Scientific Research & Development, Volume 2,Issue 02,2014 .Pages 36-39.
How to cite this paper
@article{1700885,
author = {Dr. S. Sivaprakasam, Dr. N. Nagarajan, Dr. K. Karthikeyan},
title = {Rainfall - Runoff Modeling Using Artificial Neural Network Of Perumal Tank, Cuddalore District, Tamil Nadu, India},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {2},
number = {6},
pages = {103-108},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1700885.pdf},
abstract = {Rainfall-runoff models are highly useful for water resources planning, development and flood mitigation. Rainfall-runoff analysis is quite difficult due to presence of complex nonlinear relationship in the transformation of rainfall to run-off however runoff analysis is very important for the predication of natural calamities like floods and drought. A rainfall-runoff model is a mathematical model describing the rainfall-runoff relations of a catchment area, drainage basin or washed. In the model calculates the rainfall into runoff. In rainfall-runoff modelling SCS-CN (Soil Conservation Service ? Curve Number) method uses the soil information, rainfall, storm duration, soil texture, type & extent of vegetative cover and conservation practices are considered. A new dimension has been added to the modelling approach through the adoption of the ANN (Artificial Neural Network) technique as these models possess desirable attributes of universal approximation, and the ability to learn from examples. The ANN is well known as a flexible mathematical tool and has the ability to generalize patterns in precise and ambiguous input and output data sets without attempting to reach understanding as to the nature of the phenomena. In the present study a feed forward back propagation algorithm of ANN model is used for Perumal tank, Uppanar sub basin in Kurinjipadi Taluk of Cuddalore District.},
keywords = {Rainfall, Runoff, Soil Conservation Service, Curve Number, Artificial Neural Network},
month = {December},
}