Improving the accuracy of rainfall prediction using a regionalization approach and neural networks

Authors

  • Mohammad Arab Amiri Department of Geographic Information System, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran http://orcid.org/0000-0002-3758-5802
  • Christian Conoscenti Department of Earth and Marine Sciences, University of Palermo, Palermo, Italy
  • Mohammad Saadi Mesgari Department of geographic information system, Faculty of Geodesy and Geomatics Eng., and Center of Excellence in Geospatial Information Technology (CEGIT), K. N. Toosi University of Technology, Tehran, IRAN

Keywords:

Artificial Neural Networks (ANN), Chaharmahal and Bakhtiari province, Cluster Analysis (CA), Precipitation

Abstract

Spatial and temporal analysis of precipitation patterns has become an intense research topic in contemporary climatology. Increasing the accuracy of precipitation prediction can have valuable results for decision-makers in a specific region. Hence, studies about precipitation prediction on a regional scale are of great importance. Artificial Neural Networks (ANN) have been widely used in climatological applications to predict different meteorological parameters. In this study, a method is presented to increase the accuracy of neural networks in precipitation prediction in Chaharmahal and Bakhtiari Province in Iran. For this purpose, monthly precipitation data recorded at 42 rain gauges during 1981-2012 were used. The stations were first clustered into well-defined groupings using Principal Component Analysis (PCA) and Cluster Analysis (CA), and
then one separate neural network was applied to each group of stations. Another neural network model was also developed and applied to all the stations in order to measure the accuracy of the proposed model. Statistical results showed that the presented model produced better results in comparison to the second model.

Author Biography

Christian Conoscenti, Department of Earth and Marine Sciences, University of Palermo, Palermo, Italy

Department of Earth and Marine Sciences, University of Palermo, Palermo, Italy

References

Adamowski, J., Adamowski, K. & Prokoph, A. (2013).

Quantifying the spatial temporal variability of annual

streamflow and meteorological changes in eastern Ontario

and southwestern Quebec using wavelet analysis and GIS.

Journal of Hydrology, 499: 27-40.

Alahbakhshian Farsani, P., Habibnejad Roshan, M.,

Vahbzade, G. & Solaimani, K. (2013). Investigation of

Trend of Precipitation Variation Using Non-Parametric

Methods in Charmahal O Bakhtiari Province. International

Journal of Advanced Biological and Biomedical Research,

(5): 547-555.

Alexander, L., Zhang, X., Peterson, T., Caesar, J.,

Gleason, B., et al. (2006). Global observed changes in daily

climate extremes of temperature and precipitation. Journal

of Geophysical Research: Atmospheres, 111(D5): 1-22.

Arab Amiri, M., Amerian, Y. & Mesgari, M.S. (2016).

Spatial and temporal monthly precipitation forecasting

using wavelet transform and neural networks, Qara-Qum

catchment, Iran. Arabian Journal of Geosciences, 9(5):

Arab Amiri, M. & Mesgari, M.S. (2016). Spatial

variability analysis of precipitation in northwest Iran.

Arabian Journal of Geosciences, 9(11): 578.

Azadi, S. & Sepaskhah, A.R. (2012). Annual precipitation

forecast for west, southwest, and south provinces of Iran

using artificial neural networks. Theoretical and Applied

Climatology, 109(1-2): 175-189.

Bai, X., Wang, Y., Jiang, H., Liao, Z., Xiong, Y., et al.

(2014). Mining high-temperature event space-time regions

in geo-referenced temperature series data. 11th International

Conference on Fuzzy Systems and Knowledge Discovery

(FSKD). IEEE, 671-676.

Caliński, T. & Harabasz, J. (1974). A dendrite method

for cluster analysis. Communications in Statistics-theory

and Methods, 3(1): 1-27.

Coulibaly, P., Anctil, F. & Bobee, B. (2000). Daily

reservoir inflow forecasting using artificial neural

networks with stopped training approach. Journal of

Hydrology, 230(3): 244-257.

Deshpande, R.R. (2012). On the rainfall time series

prediction using Multilayer Perceptron Artificial Neural

Network. International Journal of Emerging Technology

and Advanced Engineering, 2(1): 2250-2459.

Devasthale, A. & Norin, L. (2014). The large-scale spatiotemporal

variability of precipitation over Sweden observed

from the weather radar network. Atmospheric Measurement

Techniques, 7(6): 1605-1617.

Dubey, A.D. (2015). Artificial neural network models for

rainfall prediction in Pondicherry. International Journal of

Computer Applications, 120(3): 30-35.

Hung, N.Q., Babel, M.S., Weesakul, S. & Tripathi, N.

(2009). An artificial neural network model for rainfall

forecasting in Bangkok, Thailand. Hydrology and Earth

System Sciences, 13(8): 1413-1425.

Kohler, M.A. (1949). Double-mass analysis for testing the

consistency of records and for making adjustments. Bulletin

of the American Meteorological Society, 30: 188-189.

Kuligowski, R.J. & Barros, A.P. (1998). Experiments in

short-term precipitation forecasting using artificial neural

networks. Monthly Weather Review, 126(2): 470-482.

Minsky, M. & Papert, S. (1969). Perceptrons. An

introduction to computational geometry. MIT Press,

Cambridge, MA. Pp. 258.

Mislan, Haviluddin, Hardwinarto, S., Sumaryono &

Aipassa, M. (2015). Rainfall monthly prediction based on

Artificial Neural Network: A case study in Tenggarong

Station, East Kalimantan, Indonesia. Procedia Computer

Science, 59: 142-151.

Nabavi, R., Naeini, K.M., Zebardast, N. & Hashemi, H.

(2014). Epidemiological study of gastrointestinal helminthes of canids in Chaharmahal and Bakhtiari

Province of Iran. Iranian Journal of Parasitology, 9(2): 276-

Nanda, S.K., Tripathy, D.P., Nayak, S.K. & Mohapatra,

S. (2013). Prediction of rainfall in India using Artificial

Neural Network (ANN) models. International Journal of

Intelligent Systems and Applications, 5(12): 1-22.

Nayak, D.R., Mahapatra, A. & Mishra, P. (2013). A

survey on rainfall prediction using artificial neural network.

International Journal of Computer Applications, 72(16).

Richman, M.B. (1986). Rotation of principal components.

Journal of Climatology, 6: 293-335.

Sharma, A. & Nijhawan, G. (2015). Rainfall prediction

using neural network. International Journal of Computer

Science Trends and Technology, 3(3): 65-69.

Vamsidhar, E., Varma, K., Rao, P.S. & Satapati, R.

(2010). Prediction of rainfall using backpropagation neural

network model. International Journal on Computer Science

and Engineering, 2(4): 1119-1121.

Weerasinghe, H., Premaratne, H. & Sonnadara, D.

(2007). A neural network based rainfall forecasting system

using multiple stations. 25th National IT Conference,

Colombo, Sri Lanka. 82-86.

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Published

11-11-2018

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Section

Earth & Environment