Multiple Linear Regressions for Predicting Rainfall for Bangladesh

MAI Navid; NH Niloy

doi:doi:10.11648/j.com.20180601.11

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Multiple Linear Regressions for Predicting Rainfall for Bangladesh

MAI Navid, NH Niloy

Published in Communications (Volume 6, Issue 1)

Received: 22 November 2017 Accepted: 5 December 2017 Published: 6 February 2018

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Abstract

Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.

Published in	Communications (Volume 6, Issue 1)
DOI	10.11648/j.com.20180601.11
Page(s)	1-4
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), 2018. Published by Science Publishing Group

Keywords

Multiple Linear Regression, Data Mining, Rainfall Prediction

References

[1]	Z. ismail, et.al, (2009) “Forecasting Gold Prices Using Multiple Linear Regression Method” in American Journal of Applied Sciences. 6(8): 1509-1514.
[2]	Paras et.al, (2012) “A Simple Weather Forecasting Model Using Mathematical Regression” in Bangladeshn Research Journal of Extension Education Special Issue (Volume I). January, 2012.
[3]	Ozlem Terzi, (2012) “Monthly Rainfall Estimation Using Data-Mining Process” in Hindawi Publishing Corporation Applied Computational Intelligence and Soft Computing. Volume 2012, 6.
[4]	Wint Thida Zaw, et.al. (2008) “Empirical Statistical Modeling of Rainfall Prediction over Myanmar” in World Academy of Science, Engineering and Technology. 22. 2008-10-270.
[5]	S. Nkrintra, et al., (2005) “Seasonal Forecasting of Thailand Summer Monsoon Rainfall”, in International Journal of Climatology, Vol. 25, Issue 5, American Meteorological Society, 2005, pp. 649-664.
[6]	http://math.owu.edu-MCURCSM-papers-paper7 retrieved on 23/04/2014.
[7]	H. Hasani,et al, (2008) A New Approach to Polynomial Regression and Its Application to Physical growth of Human Height.
[8]	http://www.biochemia-medica.com/content/standard-error-meaningand-interpretation retrieved on 25/04/2014.
[9]	http://cs.gmu.edu/cne/modules/dau/stat/regression/multregsn/mreg_2_frm.htmlretrievedon25/04/2014
[10]	Khandelwal, N et.al (2012) “Climatic Assessment of Rajasthan’s Region for Drought with Concern of Data Mining Techniques” in International Journal Of Engineering Research and Application. 2(5): 1695-1697.
[11]	http://blog.minitab.com/blog/adventures-in-statistics/regressionanalysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit retrieved on 24/04/2014.
[12]	http://www.Bangladeshwaterportal.org/articles/district-wise-monthlyrainfall-data-2004-2010-list-raingauge-stations-Bangladesh-meteorological retrieved on 02/03/2014.
[13]	http://mathbits.com/MathBits/TISection/Statistics2/correlation.htm retrieved on 24/04/2014

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APA Style

MAI Navid, NH Niloy. (2018). Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications, 6(1), 1-4. https://doi.org/10.11648/j.com.20180601.11

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ACS Style

MAI Navid; NH Niloy. Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications. 2018, 6(1), 1-4. doi: 10.11648/j.com.20180601.11

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AMA Style

MAI Navid, NH Niloy. Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications. 2018;6(1):1-4. doi: 10.11648/j.com.20180601.11

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@article{10.11648/j.com.20180601.11,
  author = {MAI Navid and NH Niloy},
  title = {Multiple Linear Regressions for Predicting Rainfall for Bangladesh},
  journal = {Communications},
  volume = {6},
  number = {1},
  pages = {1-4},
  doi = {10.11648/j.com.20180601.11},
  url = {https://doi.org/10.11648/j.com.20180601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20180601.11},
  abstract = {Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.},
 year = {2018}
}

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TY  - JOUR
T1  - Multiple Linear Regressions for Predicting Rainfall for Bangladesh
AU  - MAI Navid
AU  - NH Niloy
Y1  - 2018/02/06
PY  - 2018
N1  - https://doi.org/10.11648/j.com.20180601.11
DO  - 10.11648/j.com.20180601.11
T2  - Communications
JF  - Communications
JO  - Communications
SP  - 1
EP  - 4
PB  - Science Publishing Group
SN  - 2328-5923
UR  - https://doi.org/10.11648/j.com.20180601.11
AB  - Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.
VL  - 6
IS  - 1
ER  -

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Author Information

MAI Navid

Department of Science, Ruhea College Rangpur, Bangladesh
NH Niloy

Department of Science, Ruhea College Rangpur, Bangladesh

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Plain Text BibTeX RIS

APA Style

MAI Navid, NH Niloy. (2018). Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications, 6(1), 1-4. https://doi.org/10.11648/j.com.20180601.11

Copy | Download

ACS Style

MAI Navid; NH Niloy. Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications. 2018, 6(1), 1-4. doi: 10.11648/j.com.20180601.11

Copy | Download

AMA Style

MAI Navid, NH Niloy. Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications. 2018;6(1):1-4. doi: 10.11648/j.com.20180601.11

Copy | Download

@article{10.11648/j.com.20180601.11,
  author = {MAI Navid and NH Niloy},
  title = {Multiple Linear Regressions for Predicting Rainfall for Bangladesh},
  journal = {Communications},
  volume = {6},
  number = {1},
  pages = {1-4},
  doi = {10.11648/j.com.20180601.11},
  url = {https://doi.org/10.11648/j.com.20180601.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20180601.11},
  abstract = {Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Multiple Linear Regressions for Predicting Rainfall for Bangladesh
AU  - MAI Navid
AU  - NH Niloy
Y1  - 2018/02/06
PY  - 2018
N1  - https://doi.org/10.11648/j.com.20180601.11
DO  - 10.11648/j.com.20180601.11
T2  - Communications
JF  - Communications
JO  - Communications
SP  - 1
EP  - 4
PB  - Science Publishing Group
SN  - 2328-5923
UR  - https://doi.org/10.11648/j.com.20180601.11
AB  - Agricultural economy is largely based upon crop productivity and rainfall. For analyzing the crop productivity, rainfall prediction is require and necessary to all farmers. Rainfall Prediction is the application of science and technology to predict the state of the atmosphere. It is important to exactly determine the rainfall for effective use of water resources, crop productivity and pre planning of water structures. Data mining might be used to make precise predictions for rainfalls. Most widely used techniques for rainfall is clustering, artificial neural networks, linear regression etc. In this article multiple linear regressions used for predicting rainfall in Bangladesh.
VL  - 6
IS  - 1
ER  -

Copy | Download