JWARP  Vol.5 No.1 , January 2013
Rainfall Prediction Model Improvement by Fuzzy Set Theory
ABSTRACT

This paper presents the improvement of the fuzzy inference model primarily developed for predicting rainfall with data from United States Department of Agriculture (USDA) Soil Climate Analysis Network (SCAN) Station at the Alabama Agricultural and Mechanical University (AAMU) Campus for the year 2004. The primary model was developed with Fuzzy variables selected based on the degree of association of different factors with various combinations causing rainfall. An increase in wind speed (WS) and a decrease in temperature (TP) when compared between the ith and (i-1)th day were found to have a positive relation with rainfall. Results of the model showed better performance after introducing the threshold values of 1) relative humidity (RH) of the ith day; 2) humidity increase (HI) when compared between the ith and (i-1)th day; and 3) product (P) of increase in wind speed (WS) and decrease in temperature (TP) when compared between the ith and (i-1)th day. In case of the improved model, errors between actual and calculated amount of rainfall (RF) were 1.20%, 2.19%, and 9.60% when using USDA-SCAN data from AAMU campus for years 2003, 2004 and 2005, respectively. The improved model was tested at William A. Thomas Agricultural Research Station (WTARS) and Bragg farm in Alabama to check the applicability of the model. The errors between the actual and calculated amount of rainfall (RF) were 3.20%, 5.90%, and 1.66% using USDA-SCAN data from WATARS for years 2003, 2004, and 2005, respectively. Similarly, errors were 10.37%, 11.69%, and 25.52% when using SCAN data from Bragg farm for years 2004, 2005, and 2006, respectively. The primary model yielded the value of error equals 12.35% using USDA- SCAN data from AAMU campus for 2004. The present model performance was proven to be better than the primary model.


Cite this paper
M. Hasan, X. Shi, T. Tsegaye, N. Ahmed and S. Khan, "Rainfall Prediction Model Improvement by Fuzzy Set Theory," Journal of Water Resource and Protection, Vol. 5 No. 1, 2013, pp. 1-11. doi: 10.4236/jwarp.2013.51001.
References
[1]   Z. Sen and A. Altunkaynak, “Fuzzy System Modeling of Drinking Water Consumption Prediction,” Expert Systems with Applications, Vol. 36, No. 8, 2009, pp. 10801-11400. doi:10.1016/j.eswa.2009.05.025

[2]   A. Altunkaynak, M. ?zger and M. Cakmakci, “Water Consumption Prediction of Istanbul City by Using Fuzzy Logic Approach,” Water Resources Management, Vol. 19, No. 5, 2005, pp. 641-654. doi:10.1007/s11269-005-7371-1

[3]   J. Kiska, M. Gupla and P. Nikiforuk, “Energetic Stability of Fuzzy Dynamic Systems,” IEEE Transactions on System, Men and Cybernetics, Vol. 15, No. 6, 1985, pp. 783-792. doi:10.1109/TSMC.1985.6313463

[4]   E. H. Mamdani, “Application of the Fuzzy Logic to Approximate Reasoning Using Linguistic Synthesis,” IEEE Transactions and Computers, Vol. C-26, No. 12, 1977, pp. 1182-1191. doi:10.1109/TC.1977.1674779

[5]   J. T. Ross, “Fuzzy Logic with Engineering Applications,” McGraw-Hill Inc., New York, 1995.

[6]   Z. Sen and A. Altunkaynak, “Fuzzy Awakening in Rainfall-Runoff Modeling,” Nordic Hydrology, Vol. 35, No. 1, 2004, pp. 31-43.

[7]   L. A. Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8, No. 3, 1965, pp. 338-352. doi:10.1016/S0019-9958(65)90241-X

[8]   M. Hasan, T. Tsegaye, X. Shi, G. Schaefer and G. Taylor, “Model for Predicting Rainfall by Fuzzy Set Theory Using USDA-SCAN Data,” Agricultural Water Manage- ment, Vol. 95, No. 12, 2008, pp. 1350-1360. doi:10.1016/j.agwat.2008.07.015

[9]   M. Hasan, M. Mizutani, A. Goto and H. Matsui, “A Model for Determination of Intake Flow Size: Development of Optimum Operational Method for Irrigation Using Fuzzy Set Theory (1),” System Nogaku: Journal of Japan Agricultural System Society, Vol. 11, No. 1, 1995, pp. 1-13.

[10]   T. Hasan and S. Zenkai, “A New Modeling Approach for Predicting the Maximum Daily Temperature from a Time Series,” Turkish Journal of Engineering and Environmental Science, Vol. 23, No. 3, 1999, pp. 173-180.

 
 
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