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%20%20%20%20Prediction%20of%20rainfall%20using%20autoregressive%20integrated%20moving%20average%20model:%20Case%20of%20Kinshasa%20city%20(Democratic%20Republic%20of%20the%20Congo),%20from%20the%20period%20of%201970%20to%202009
Research article
  

Prediction of rainfall using autoregressive integrated moving average model: Case of Kinshasa city (Democratic Republic of the Congo), from the period of 1970 to 2009


Dedetemo Kimilita Patrick, Phuku Phuati Edmond, Tshitenge Mbwebwe Jean-Marie, Efoto Eale Louis, Koto-te-Nyiwa Ngbolua

1Faculty of Petroleum & Gas, University of Kinshasa, Kinshasa XI, D.R. Congo
2Department of Physics, Faculty of Science, University of Kinshasa, Kinshasa XI, D.R. Congo
3Department of Biology, Faculty of Science, University of Kinshasa, Kinshasa XI, D.R. Congo.


Corresponding author :

Dr. Koto-te-Nyiwa Ngbolua,
Email:

Received: September 11, 2014,   Accepted: October 2, 2014,   Published: October 3, 2014.


Abstract:

Rainfall is natural climatic phenomena for which prediction constitutes a great challenge nowadays. Its forecast is of particular relevance to agriculture and medicinal plants growth and development, which contribute significantly to the economy of Africa. Rainfall is highly non-linear and complicated phenomena, which require mathematical modelling and simulation for its accurate prediction. The present study examined the monthly precipitation using the Box-Jenkins methodology. The monthly precipitations data were collected from Binza Meteorological station of Kinshasa (Democratic Republic of the Congo) during the year 1970 to 2009. The results of the estimated parameters revealed that ARIMA (5, 1, 1) model is appropriate for the series. In the first analysis, we standardized this time series, then we have modeled the resulting series by model ARIMA (5, 1,1). In the second analysis, we carried out a modeling of these quantities using ARIMA model according to three processes: Identification of the model, validation of the model and estimate of the model. In order to compare the results of these two modeling, the average relative quadratic errors (er) and the average quadratic errors (EM) of the forecast adjustment were evaluated. These models appear equivalent in terms of these two errors. In the third analysis, we established a forecast of various corresponding years and we show that the event-based estimation approach yields better forecasts. It can be therefore concluded that the use of ARIMA model as tool for predicting rainfall could help in agricultural research development and in predicting the best period for the harvest of medicinal plant samples for phytotherapy (the quality/quantity of secondary metabolites and bioactivity). This model also makes it possible to predict the implication of rainfall on the lifestyle of the Kinshasa inhabitants.


Keywords: Rainfall, forecast, statistics, ARIMA model


Citation:

Koto-te-Nyiwa Ngbolua, et al.. (2014). Prediction of rainfall using autoregressive integrated moving average model: Case of Kinshasa city (Democratic Republic of the Congo), from the period of 1970 to 2009. J. of Computation in Biosciences and Engineering. V2I1. DOI: 10.15297/JCLS.V2I1.1


Copyright:

© 2014 Koto-te-Nyiwa Ngbolua. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


      
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      Journal of Computation in Biosciences and Engineering