Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River

Sharad Patel; M. K. Hardaha; Mukesh K. Seetpal; K. K. Madankar

doi:doi:10.11648/j.ajwse.20160201.11

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Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River

Sharad Patel, M. K. Hardaha, Mukesh K. Seetpal, K. K. Madankar

Published in American Journal of Water Science and Engineering (Volume 2, Issue 1)

Received: 6 December 2015 Accepted: 4 January 2016 Published: 1 March 2016

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Abstract

This paper is based on the study of multiple linear regression based technique in determining the rainfall-runoff relations. Without considering the temperature, topography or other parameters of the study area, simply using the data of rainfall and runoff to predict the future runoff is the key characteristic of this technique. Ignoring the parameters of temperature, topography may lead to inaccuracy in forecasting future runoff, but the use of Multiple linear regression based technique provide a simple and fast way to determine the runoff. In the present investigation, 14 years data on rainfall and runoff at the Balaghat district of Madhya Pradesh have been analyzed to develop regression models for stream flow estimation with rainfall as input and different regression model tested with varying input length of data record.

Published in	American Journal of Water Science and Engineering (Volume 2, Issue 1)
DOI	10.11648/j.ajwse.20160201.11
Page(s)	1-5
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Multiple Linear Regression Model, Correlation Coefficient, Standard Error

References

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[5]	Holtan, H., Stiltner, G., Hensen, W., & Lopez, N. (1975). Revised model of Watershed Hydrology. Washington, D. C.: USDA-ARS Tech. Bulletin No. 1518.
[6]	Jarboe, J., & Haan, C. (1974). Calibration of Water yield model for small ungauged watersheds. Water Resources Research, 2, 256-262.
[7]	Jones, J. (1976). Physical data for catchment models. Nordic Hydrol., 245-164.
[8]	Kothyari, U. (1995). Estimation of monthly runoff from small catchments in India. Hydrological Sciences – Journal - des Sciences Hydrologigues, 40, 533-541.
[9]	Lane, L., Diskin, M., & Renard, K. (1971). Input- Output relationship for an ephemeral stream channel system. Journal of Hydrology, 13, 22-40.
[10]	Lindner- Lunsford, J., & Ellis, S. (1987). Comparison of conceptually based and regression rainfall- runoff models, Denver Metropolitan Area, Colorado, and application in urban areas. U. S. Geological Survey, Denver, CO.
[11]	Magette, W., Shanholtz, V., & Carr, J. (1976). Estimation selected parameters for the Kentucky Watershed model from watershed characterstics. Water Resources Research, 12(3), 462-476.
[12]	Nawaz, N., & Adeloye, A. (1999). Evaluation of monthly runoff estimated by a rainfall- runoff model for reservoir yield assessment. Hydrological Sciences—Journal—des Sciences Hydrologiques, 1, 44.
[13]	Newton, D., & Herrin, J. (1983). Estimating flood frequencies at ungauged loaction. Hydraulic Engineering (pp. 528-533). New York: American Society of Civil Engineers.
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[16]	Wharton, G., & Tomlinson, J. (1999). Flood discharge estimation from river channel dimensions: results of applications in Java, Burundi, Ghana and Tanzania. Hydrological Sciences—Journal—des Sciences Hydrologiques, 1, 44.
[17]	Xu, C., Seibert, J., & Halldin, S. (1996). Regional water balance modelling in the NOPEX area test on parameter estimation using catchment characteristics. Journal of Hydrology.
[18]	Xu, C., & V. P., Singh. (1998). A review on monthly water balance models for water resources investigations. Water Resources management, 12, 31-50.

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APA Style

Sharad Patel, M. K. Hardaha, Mukesh K. Seetpal, K. K. Madankar. (2016). Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River. American Journal of Water Science and Engineering, 2(1), 1-5. https://doi.org/10.11648/j.ajwse.20160201.11

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ACS Style

Sharad Patel; M. K. Hardaha; Mukesh K. Seetpal; K. K. Madankar. Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River. Am. J. Water Sci. Eng. 2016, 2(1), 1-5. doi: 10.11648/j.ajwse.20160201.11

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AMA Style

Sharad Patel, M. K. Hardaha, Mukesh K. Seetpal, K. K. Madankar. Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River. Am J Water Sci Eng. 2016;2(1):1-5. doi: 10.11648/j.ajwse.20160201.11

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@article{10.11648/j.ajwse.20160201.11,
  author = {Sharad Patel and M. K. Hardaha and Mukesh K. Seetpal and K. K. Madankar},
  title = {Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River},
  journal = {American Journal of Water Science and Engineering},
  volume = {2},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.ajwse.20160201.11},
  url = {https://doi.org/10.11648/j.ajwse.20160201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajwse.20160201.11},
  abstract = {This paper is based on the study of multiple linear regression based technique in determining the rainfall-runoff relations. Without considering the temperature, topography or other parameters of the study area, simply using the data of rainfall and runoff to predict the future runoff is the key characteristic of this technique. Ignoring the parameters of temperature, topography may lead to inaccuracy in forecasting future runoff, but the use of Multiple linear regression based technique provide a simple and fast way to determine the runoff. In the present investigation, 14 years data on rainfall and runoff at the Balaghat district of Madhya Pradesh have been analyzed to develop regression models for stream flow estimation with rainfall as input and different regression model tested with varying input length of data record.},
 year = {2016}
}

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TY  - JOUR
T1  - Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River
AU  - Sharad Patel
AU  - M. K. Hardaha
AU  - Mukesh K. Seetpal
AU  - K. K. Madankar
Y1  - 2016/03/01
PY  - 2016
N1  - https://doi.org/10.11648/j.ajwse.20160201.11
DO  - 10.11648/j.ajwse.20160201.11
T2  - American Journal of Water Science and Engineering
JF  - American Journal of Water Science and Engineering
JO  - American Journal of Water Science and Engineering
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2575-1875
UR  - https://doi.org/10.11648/j.ajwse.20160201.11
AB  - This paper is based on the study of multiple linear regression based technique in determining the rainfall-runoff relations. Without considering the temperature, topography or other parameters of the study area, simply using the data of rainfall and runoff to predict the future runoff is the key characteristic of this technique. Ignoring the parameters of temperature, topography may lead to inaccuracy in forecasting future runoff, but the use of Multiple linear regression based technique provide a simple and fast way to determine the runoff. In the present investigation, 14 years data on rainfall and runoff at the Balaghat district of Madhya Pradesh have been analyzed to develop regression models for stream flow estimation with rainfall as input and different regression model tested with varying input length of data record.
VL  - 2
IS  - 1
ER  -

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Author Information

Sharad Patel

Water Resources Division, Department of Civil Engineering, IIT Bombay, Mumbai, Maharastra, India
M. K. Hardaha

Department of Soil and Water Engineering, College of Agricultural Engineering, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, India
Mukesh K. Seetpal

Department of Civil Engineering, AKS University, Satna, Madhya Pradesh, India
K. K. Madankar

NM Sadguru Water and Development Foundation, Chosala, Gujarat, India

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APA Style

Sharad Patel, M. K. Hardaha, Mukesh K. Seetpal, K. K. Madankar. (2016). Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River. American Journal of Water Science and Engineering, 2(1), 1-5. https://doi.org/10.11648/j.ajwse.20160201.11

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ACS Style

Sharad Patel; M. K. Hardaha; Mukesh K. Seetpal; K. K. Madankar. Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River. Am. J. Water Sci. Eng. 2016, 2(1), 1-5. doi: 10.11648/j.ajwse.20160201.11

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AMA Style

Sharad Patel, M. K. Hardaha, Mukesh K. Seetpal, K. K. Madankar. Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River. Am J Water Sci Eng. 2016;2(1):1-5. doi: 10.11648/j.ajwse.20160201.11

Copy | Download

@article{10.11648/j.ajwse.20160201.11,
  author = {Sharad Patel and M. K. Hardaha and Mukesh K. Seetpal and K. K. Madankar},
  title = {Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River},
  journal = {American Journal of Water Science and Engineering},
  volume = {2},
  number = {1},
  pages = {1-5},
  doi = {10.11648/j.ajwse.20160201.11},
  url = {https://doi.org/10.11648/j.ajwse.20160201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajwse.20160201.11},
  abstract = {This paper is based on the study of multiple linear regression based technique in determining the rainfall-runoff relations. Without considering the temperature, topography or other parameters of the study area, simply using the data of rainfall and runoff to predict the future runoff is the key characteristic of this technique. Ignoring the parameters of temperature, topography may lead to inaccuracy in forecasting future runoff, but the use of Multiple linear regression based technique provide a simple and fast way to determine the runoff. In the present investigation, 14 years data on rainfall and runoff at the Balaghat district of Madhya Pradesh have been analyzed to develop regression models for stream flow estimation with rainfall as input and different regression model tested with varying input length of data record.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Multiple Linear Regression Model for Stream Flow Estimation of Wainganga River
AU  - Sharad Patel
AU  - M. K. Hardaha
AU  - Mukesh K. Seetpal
AU  - K. K. Madankar
Y1  - 2016/03/01
PY  - 2016
N1  - https://doi.org/10.11648/j.ajwse.20160201.11
DO  - 10.11648/j.ajwse.20160201.11
T2  - American Journal of Water Science and Engineering
JF  - American Journal of Water Science and Engineering
JO  - American Journal of Water Science and Engineering
SP  - 1
EP  - 5
PB  - Science Publishing Group
SN  - 2575-1875
UR  - https://doi.org/10.11648/j.ajwse.20160201.11
AB  - This paper is based on the study of multiple linear regression based technique in determining the rainfall-runoff relations. Without considering the temperature, topography or other parameters of the study area, simply using the data of rainfall and runoff to predict the future runoff is the key characteristic of this technique. Ignoring the parameters of temperature, topography may lead to inaccuracy in forecasting future runoff, but the use of Multiple linear regression based technique provide a simple and fast way to determine the runoff. In the present investigation, 14 years data on rainfall and runoff at the Balaghat district of Madhya Pradesh have been analyzed to develop regression models for stream flow estimation with rainfall as input and different regression model tested with varying input length of data record.
VL  - 2
IS  - 1
ER  -

Copy | Download