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Performance of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for the Prediction of Monthly Maximum Rainfall in Benin City, Nigeria

Yıl 2019, Cilt: 3 Sayı: 1, 21 - 37, 29.03.2019

Öz


The focus of the study was to investigate the capability of linear and non-linear regression techniques for long-term rainfall prediction. Of the linear regression techniques, multiple linear regression method was employed. One of the non-linear regression techniques being widely used in time series prediction is Artificial Neural Networks (ANN) approach which has the ability of mapping between input and output patterns. ANNs are among the numerous empirical models available and have proven to be especially good in modelling time-dependent systems.




The study area was restricted to Benin City. Monthly rainfall data, wind speed, evaporation, temperature and relative humidity for the period of 1981 to 2015 spanning to about thirty-four (34) years was collected processed and used for the analysis. Data analysis tools, namely; EViews, SPSS, and MATLAB were employed to conduct the analysis.




Results of the descriptive statistics show a marked variation in the mean and standard deviation of the data used. Rainfall, for example, had a mean value of 459.643 and standard deviation of 1.0655E2. The bell-shaped configuration observed in the histogram plot of the variables revealed that the climatic variables used in the study are statistically normally distributed. On the performance of multiple linear regression (MLR) and artificial neural network (ANN), it was observed that artificial neural network performed better than multiple linear regressions. This conclusion was based on the calculated value of the coefficient of determination (R2) for which ANN was 0.9999 and MLR was 0.1755. The performance of ANN compared to MLR was based on the non-linear dependence of rainfall on other climatic variables such as temperature, wind speed, relative humidity, and vapour pressure.




Kaynakça

  • French, M. N., Krajewski, W. F., and Cuykendall, R. R (1992): Rainfall forecasting in space and time using neural network, Journal of Hydrology, vol. 137, pp: 1–31.
  • Hsu, K; Gupta, H. V and Sorooshian, S (2015): Artificial neural network modeling of the rainfall-runoff process, Water Resources Research Journal; vol. 31(10), pp: 2517–2530
  • Hung, N. Q; Babel, M. S; Weesakul, S and Tripathi, N. K (2008): An artificial neural network model for rainfall forecasting in Bangkok, Thailand; Hydrology Earth System Sciences Discussion 5, pp: 183–218
  • Kin, C; Luk, J; Ball, E and Sharma, A (2001): An Application of Artificial Neural Networks for Rainfall Forecasting; Meteorological and Atmospheric Physics Journal, vol. 33, pp: 883-699.Leahy, K (2001): Multicollinearity: When the solution is the problem, in: Data mining cookbook: Modelling data for marketing, risk and customer relationship management, edited by: Rud, O. P., John Wiley and Sons, New York, pp: 106–108
  • Lee, S; Cho, S and Wong, P.M (1998): Rainfall prediction using artificial neural network“, J. Geog. Inf. Decision Anal, vol. 2, pp: 233–242Levi, D. B.; Julie, E. K.; Olsen, J. R.; Pulwarty, R. S.; Raff, D. A.; Turnipseed, D. P.; Webb, R. S and Kathleen D. W (2009); Climate Change and Water Resources Management: A Federal Perspective, circular 1331, pp: 1 – 72
  • Mekanika, F; Leeb, T. S and Imteaza, M A (2011): Rainfall modeling using Artificial Neural Network for a mountainous region in West Iran;19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011; http://mssanz.org.au/modsim2011, PP: 23-45
  • Okhakhu, P.A. (2014): Environmental and Human Challenges in the Niger-Delta Region of Nigeria, Journal of Environment and Earth Science, Vol. 4(23), pp: 112 - 134Raes, D; Willens, P and Gbaguidi (2006), Rainbow – A software package for analyzing data and testing the homogeneity of historical data sets, vol. 1, pp: 1-15
  • Shaymaa, A.A (2014): Prediction of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models; Journal of Engineering and Development, Vol. 18, No.1, ISSN 1813- 7822; pp: 129-143
  • Wong, K.W; Wong, P.M; Gedeon, T. D. and Fung, C. C (2003): Rainfall Prediction Using Soft Computing Technique, Soft Computing Journal, vol.7, pp: 434 – 438.
Yıl 2019, Cilt: 3 Sayı: 1, 21 - 37, 29.03.2019

Öz

Kaynakça

  • French, M. N., Krajewski, W. F., and Cuykendall, R. R (1992): Rainfall forecasting in space and time using neural network, Journal of Hydrology, vol. 137, pp: 1–31.
  • Hsu, K; Gupta, H. V and Sorooshian, S (2015): Artificial neural network modeling of the rainfall-runoff process, Water Resources Research Journal; vol. 31(10), pp: 2517–2530
  • Hung, N. Q; Babel, M. S; Weesakul, S and Tripathi, N. K (2008): An artificial neural network model for rainfall forecasting in Bangkok, Thailand; Hydrology Earth System Sciences Discussion 5, pp: 183–218
  • Kin, C; Luk, J; Ball, E and Sharma, A (2001): An Application of Artificial Neural Networks for Rainfall Forecasting; Meteorological and Atmospheric Physics Journal, vol. 33, pp: 883-699.Leahy, K (2001): Multicollinearity: When the solution is the problem, in: Data mining cookbook: Modelling data for marketing, risk and customer relationship management, edited by: Rud, O. P., John Wiley and Sons, New York, pp: 106–108
  • Lee, S; Cho, S and Wong, P.M (1998): Rainfall prediction using artificial neural network“, J. Geog. Inf. Decision Anal, vol. 2, pp: 233–242Levi, D. B.; Julie, E. K.; Olsen, J. R.; Pulwarty, R. S.; Raff, D. A.; Turnipseed, D. P.; Webb, R. S and Kathleen D. W (2009); Climate Change and Water Resources Management: A Federal Perspective, circular 1331, pp: 1 – 72
  • Mekanika, F; Leeb, T. S and Imteaza, M A (2011): Rainfall modeling using Artificial Neural Network for a mountainous region in West Iran;19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011; http://mssanz.org.au/modsim2011, PP: 23-45
  • Okhakhu, P.A. (2014): Environmental and Human Challenges in the Niger-Delta Region of Nigeria, Journal of Environment and Earth Science, Vol. 4(23), pp: 112 - 134Raes, D; Willens, P and Gbaguidi (2006), Rainbow – A software package for analyzing data and testing the homogeneity of historical data sets, vol. 1, pp: 1-15
  • Shaymaa, A.A (2014): Prediction of Monthly Rainfall In Kirkuk Using Artificial Neural Network And Time Series Models; Journal of Engineering and Development, Vol. 18, No.1, ISSN 1813- 7822; pp: 129-143
  • Wong, K.W; Wong, P.M; Gedeon, T. D. and Fung, C. C (2003): Rainfall Prediction Using Soft Computing Technique, Soft Computing Journal, vol.7, pp: 434 – 438.
Toplam 9 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Articles
Yazarlar

Idowu Ilaboya 0000-0002-8982-7404

Yayımlanma Tarihi 29 Mart 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 3 Sayı: 1

Kaynak Göster

IEEE I. Ilaboya, “Performance of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) for the Prediction of Monthly Maximum Rainfall in Benin City, Nigeria”, IJESA, c. 3, sy. 1, ss. 21–37, 2019.

ISSN 2548-1185
e-ISSN 2587-2176
Period: Quarterly
Founded: 2016
Publisher: Nisantasi University
e-mail:ilhcol@gmail.com