Sains Malaysiana 44(10)(2015): 1389–1396

Prediction of Lead Seven Day Minimum and Maximum Surface Air Temperature using Neural Network and Genetic Programming

(Peramalan Awalan Tujuh Hari Minimum dan Suhu Permukaan Udara Maksimum menggunakan Rangkaian Neuron dan Pengaturcaraan Genetik)

K. RAMESH¹*, R. ANITHA² & P. RAMALAKSHMI¹

¹Department of Computer Applications, Regional Centre, Anna University, Tirunelveli,

Tamil Nadu 627007, India

²Muthayammal Engineering College, Rasipuram, Namakkal District, Tamil Nadu

India

Diserahkan: 15 Oktober 2013/Diterima: 4 Ogos 2015

ABSTRACT

The determination of variance of surface air temperature is very essential since it has a direct impact on vegetation, environment and human livelihood. Forecast of surface air temperature is difficult because of the complex physical phenomenon and the random-like behavior of atmospheric system which influences the temperature event on the earth surface. In this study, forecast models based on artificial neural network (ANN) and genetic programming (GP) approaches were proposed to predict lead seven days minimum and maximum surface air temperature using the weather parameters observed at the station Chennai, India. The outcome of this study stated that models formulated using ANN approach are more accurate than genetic programming for all seven days with the highest coefficient of determination (R2), least mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) on deployment with independent test dataset. ANN models give statistically acceptable mean absolute error of 0.59oC for lead day one in minimum temperature forecast and 0.86oC variance for lead day one in maximum temperature forecast. The study also clarified that the level of accuracy of the proposed prediction models were found to be better for smaller lead days when compared with higher lead days with both approaches.

Keywords: ANN; GP; surface temperature; temperature forecast

ABSTRAK

Penentuan perbezaan suhu permukaan udara adalah sangat penting kerana ia mempunyai kesan langsung pada tumbuh-tumbuhan, alam sekitar dan kehidupan manusia. Ramalan suhu permukaan udara adalah sukar kerana fenomena fizikal yang kompleks dan perilaku rawak seperti sistem atmosfera yang mempengaruhi keadaan suhu permukaan bumi. Dalam kajian ini, peramalan model berdasarkan pendekatan rangkaian neuron tiruan (ANN) dan genetik pengaturcaraan (GP) dicadangkan untuk meramalkan awalan tujuh hari minimum serta suhu permukaan udara maksimum menggunakan parameter cuaca yang dicerap di Stesen Chennai, India. Hasil kajian ini menunjukkan bahawa model yang dirumus menggunakan pendekatan ANN adalah lebih tepat daripada genetik pengaturcaraan untuk semua tujuh hari dengan pekali penentuan tertinggi (R2), min ralat mutlak terkecil (MAE), punca min ralat kuasa dua (RMSE) dan bermakna min ralat peratusan mutlak (MAPE) pada pengerahan dengan dataset ujian bebas. Model ANN memberikan min ralat mutlak 0.59oC yang boleh diterima secara statistik untuk awalan satu hari dalam suhu peramalan minimum dan 0.86oC varians bagi satu hari dalam suhu peramalan maksimum. Kajian ini juga menjelaskan tahap ketepatan model ramalan yang dicadangkan adalah lebih baik untuk awalan hari lebih kecil jika dibandingkan dengan awalan hari lebih besar dengan kedua-dua pendekatan.

Kata kunci: ANN; GP; peramalan suhu; suhu permukaan

RUJUKAN

Abdel-Aal, R.E. 2004. Hourly temperature forecasting using abductive networks Engineering. Applications of Artificial Intelligence 17: 543-556.

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000a. Artificial neural networks in hydrology-I: Preliminary concepts. Journal of Hydrologic Engineering 5(2): 115-123.

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. 2000b. Artificial neural networks in hydrology-II: Hydrologic applications. Journal of Hydrologic Engineering 5(2): 124-137.

Benghanem, M. & Mellit, A. 2010. Radial basis function network-based prediction of global solar radiation data: Application for sizing of a stand-alone photovoltaic system Al-Madinah, Saudi Arabia. Energy 35: 3751-3762.

De, S.S., Chattopadhyay, G., Bandyopadhyay, B. & Paul, S. 2011. A neurocomputing approach to the forecasting of monthly maximum temperature over Kolkata, India using total ozone concentration as predictor. Comptes Rendus Geoscience 343: 664-676.

Fabbian, D., De Dear, R. & Lellyett, S. 2007. Application of artificial neural network forecasts to predict fog at Canberra International Airport. Weather Forecast 22: 372-381.

Hsu, K., Gupta, H.V. & Sorooshian, S. 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research 31: 2517-2530.

Hung, N.Q., Babel, M.S., Weesakul, S. & Tripathi, N.K. 2009. An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrology and Earth System Science 13: 1413-1425.

India Meteorology Department-Pune. 2008. Forecasts’ Guide.

Kashid, S.S., Subimal Ghosh & Rajib Maity. 2010. Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection. Journal of Hydrology 395: 23-38.

Koza, J.R. 1994. Genetic Programming II: Automatic Discovery of Reusable Programs. Cambridge, MA: MIT Press.

Koza, J.R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: MIT Press.

Landeras, G., Lopez, J.J., Kisi, O. & Shiri, J. 2012. Comparison of gene expression programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain). Energy Conversion and Management 62: 1-13.

Maier, H.R. & Dandy, G.C. 1996. The use of artificial neural networks for the prediction of water quality parameters. Water Resources Research 32(4): 1013-1022.

Nasseri, M., Moeini, A. & Tabesh, M. 2011. Forecasting monthly urban water demand using extended Kalman filter and genetic programming. Expert Systems with Applications 38: 7387-7395.

Nayak, P.C., Sudheer, K.P. & Jain, S.K. 2007. Rainfall-runoff modeling through hybrid intelligent system. Water Resources Research 43: W07415.

Nayak, P.C., Satyaji Rao, Y.R. & Sudheer, K.P. 2006. Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resources Management 20: 77-90.

Ortiz-Garcia, E.G., Salcedo-Sanz, S., Casanova-Mateo, C., Paniagua-Tineo, A. & Portilla-Figueras, J.A. 2012. Accurate local very short-term temperature prediction based on synoptic situation Support Vector Regression banks. Atmospheric Research 107: 1-8.

Paniagua-Tineo, A., Salcedo-Sanz, S., Casanova-Mateo, C., Ortiz-Garcia, E.G., Cony, M.A. & Hernandez-Martin, E. 2011. Prediction of daily maximum temperature using a support vector regression Algorithm. Renewable Energy 36: 3054 -3060.

Roebber, P.J., Butt, M.R. & Reinke, S.J. 2007. Real-time forecasting of snowfall using a neural network. Weather Forecast 20: 676-684.

Ruano, A.E., Crispim, E.M., Conceicao, E.Z.E. & Lucio, M.M.J.R. 2006. Prediction of building’s temperature using neural networks models. Energy and Buildings 38: 682-694.

Rumelhart, D.E., Hinton, G.E. & Williams, R.J. 1986. Learning Internal Representations by Error Propagation. Parallel distributed processing: Explorations in the microstructure of cognition 1: 318-362.

Shiri, J., Kisi, O., Landeras, G., Lopez, J.J., Nazemi, A.H. & Stuyt, L. 2012. Daily reference evapotranspiration modeling by using genetic programming approach in the Basque Country (Northern Spain). Journal of Hydrology 414: 302-316.

Smith, B.A., Hoogenboom, G. & McClendon, R.W. 2009. Artificial neural networks for automated year-round temperature prediction. Computers and Electronics in Agriculture 68: 52-61.

Stanislawska, K., Krawiec, K. & Kundzewicz, Z.W. 2012. Modeling global temperature changes with genetic programming. Computers and Mathematics with Applications 64: 3717-3728.

*Pengarang untuk surat menyurat;  email: rameshk7n@yahoo.co.in