Spatial Prediction of Monthly Precipitation in Sulaimani Governorate using Artificial Neural Network Models
Main Article Content
Abstract
ANN modeling is used here to predict missing monthly precipitation data in one station of the eight weather stations network in Sulaimani Governorate. Eight models were developed, one for each station as for prediction. The accuracy of prediction obtain is excellent with correlation coefficients between the predicted and the measured values of monthly precipitation ranged from (90% to 97.2%). The eight ANN models are found after many trials for each station and those with the highest correlation coefficient were selected. All the ANN models are found to have a hyperbolic tangent and identity activation functions for the hidden and output layers respectively, with learning rate of (0.4) and momentum term of (0.9), but with different data set sub-division into training, testing and holdout data sub-sets, and different number of hidden nodes in the hidden layer. It is found that it is not necessary that the nearest station to the station under prediction has the highest effect; this may be attributed to the high differences in elevation between the stations. It can also found that the variance is not necessary has effect on the correlation coefficient obtained.
Article Details
How to Cite
Publication Dates
References
Al-Suhaili R. H. and Ghafour Z. J., 2012, Artificial Neural Network Model to Predict Sodium Adsorption Ratio for Tigirs River in Amara, Accepted for publishing, Journal of Engineering, Collage of Engineering, University of Baghdad.
Belayneh A. M., Adamowski J. F., 2012, Standard Precipitation Index Drought Forecasting using Neural Networks, Wavelet Neural Networks and Support Vector Regression Hindawi Publishing Corporation, Journal of Applied Computational Intelligence and Soft Computing.
Bustami, R., Bessaih, N., Bong, C., Suhaili, S., 2006, Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River IAENG International /journal of Computer Science, 34:2, IJCS_34_2_10.
Dastorani, M. T., and Wright, N. G., 2002, Artificial Neural Network Based Real-Time River Flow Prediction, in the Proceedings of Hydroinformatics.
Dolling O. R., and Varas, E. A., 2002, Artificial Neural Networks for Streamflow Prediction, Journal of Hydraulic Research. 40(5), pp. 547-554.
Dozier, A., 2012, Influence of Spatial Variation in Precipitation on Artificial Neural Network Rainfall- Runoff Model.
El-Shafie A, Abdalla O., Noureldin A., Aini H. 2010 a. Performance Evaluation of a Nonlinear Error Model for Underwater Range Computation Utilizing GPS Sonobuoys, Neural Computer Application, 19(5): 272-283.
El-Shafie A, Abdelazim T, Noureldin A., 2010b, Neural Network Modeling of Time-Dependent Creep Deformations In Masonry Structures, Neural Computer Application, 19(4): 583-594.
El-Shafie, A. H., El-Shafie, A., El Mazoghi, H. G., Shehata, A., Taha, M. R., (2011), Artificial Neural Network Technique for Rainfall Forecasting Applied To Alexandria, Egypt, International Journal of the Physical Sciences Vol. 6(6), 1306-1316.
French, M., Krajewski, W., and Cuykendall, R. R.,1992, Rainfall Forecasting in Space and Time Using a Neural Network, Journal of Hydrology, 137, p1-31.
Hsu, K. L., Gupta, V. and Soroshian, S., 1995, Artificial Neural Network Modeling of the Rainfall- Runoff process, Water Resources Research, Vol.31, No. 10, p 2517-2530.
Huang, W., Murray, C., Kraus, N., and Rosati, J., 2003, Development of A Regional Artificial Neural Network Prediction for Coastal Water Level Prediction, Ocean Engineering vol. 30, pp. 2275- 2295.
Ibrahim H. M., 2012, Forecasting Models for Municipal Water Demand, Ph.D. Dissertation, Collage of Engineering, University of Sulaimani.
Luk, K. C., Ball, J. E., Sharma, A., 2000, A Study of Optimal Model Lag and Spatial Inputs to Artificial Neural Network for Rainfall Forecasting, Journal of hydrology 227: 56-65.
Luk, K. C., Ball, J. E., Sharma, A., 2001, An Application of Artificial Neural Networks for Rainfall Forecasting, Mathematical and Computer Modeling 33(2001) 683-693.
Patrick, A. R., Collins, W.G., Tissot, P.E., Drikitis A., Stearns, J., Michaud, P.R., and Cox, D.T., (2002), use of the NCEP Mesoeta Data in a Water Level Predicting Artificial Neural Network, in the Proceedings of 19th Conf. on weather Analysis and Forecasting/15th Conf. on Numerical Weather Prediction.
Saoud A. A., 2009, Water Quality Simulation by Artificial Neural Network Technique for South Al- Hammar Marsh, Iraq, M. Sc. Thises
Sobri, H., Hashim, M. K., and Irwan, A. N., 2002, Rainfall-Runoff Modeling using Artificial neural Network, Proceedings of 2nd World Engineering Congress. Kuching.
Thirumalaiah, K., and Deo, M. C., 2000, Hydrological Forecasting using Artificial Neural Network, Journal of Hydrologic Engineering, 5(2), pp. 180-189.
Tokar, A.S., and Johnson, P.A., 1999, Rainfall- Runoff Modeling using Artificial Neural Networks, Journal of Hydrologic Engineering, 4(3), pp. 223- 239.
Wright, N. G., and Dastorani, M. T., 2001, Effects o River Basin Classification on Artificial Neural Networks Based Ungauged Catchment Flood prediction, Proceedings of the International Symposium on Environmental Hydraulics, Phoenix.
Sulaimani Governorate Weather Station Records, 2004–2011. Agro-Metrological Center – General Directorate of Agricultural, Ministry of Agriculture, KRG; Iraq.