Volume 6, Issue 1, March 2018, Page: 1-4
Multiple Linear Regressions for Predicting Rainfall for Bangladesh
MAI Navid, Department of Science, Ruhea College Rangpur, Bangladesh
NH Niloy, Department of Science, Ruhea College Rangpur, Bangladesh
Received: Nov. 22, 2017;       Accepted: Dec. 5, 2017;       Published: Feb. 6, 2018
DOI: 10.11648/j.com.20180601.11      View  796      Downloads  32
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.
Multiple Linear Regression, Data Mining, Rainfall Prediction
To cite this article
MAI Navid, NH Niloy, Multiple Linear Regressions for Predicting Rainfall for Bangladesh, Communications. Vol. 6, No. 1, 2018, pp. 1-4. doi: 10.11648/j.com.20180601.11
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Z. ismail, et.al, (2009) “Forecasting Gold Prices Using Multiple Linear Regression Method” in American Journal of Applied Sciences. 6(8): 1509-1514.
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.
Ozlem Terzi, (2012) “Monthly Rainfall Estimation Using Data-Mining Process” in Hindawi Publishing Corporation Applied Computational Intelligence and Soft Computing. Volume 2012, 6.
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.
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.
http://math.owu.edu-MCURCSM-papers-paper7 retrieved on 23/04/2014.
H. Hasani,et al, (2008) A New Approach to Polynomial Regression and Its Application to Physical growth of Human Height.
http://www.biochemia-medica.com/content/standard-error-meaningand-interpretation retrieved on 25/04/2014.
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.
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.
http://www.Bangladeshwaterportal.org/articles/district-wise-monthlyrainfall-data-2004-2010-list-raingauge-stations-Bangladesh-meteorological retrieved on 02/03/2014.
http://mathbits.com/MathBits/TISection/Statistics2/correlation.htm retrieved on 24/04/2014
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