This paper presents the improvement of the fuzzy inference model for predicting rainfall. Fuzzy rule based system is used in this study to predict rainfall. Fuzzy inference is the actual procedure of mapping with a given set of input and output through a set of fuzzy systems. Two operations were performed on the fuzzy logic model; the fuzzification operation and defuzzification operation. This study is obtaining two input variables and one output variable. The input variables are temperature and wind speed at a particular time and output variable is the amount of predictable rainfall. Temperature, wind speed and rainfall have to construct eight equations for different categories and which are shows the diagram of the graph. Fuzzy levels and membership functions obtained after minimum composition of inference part of the fuzzifications done for temperature and wind speed are considered as they represent the environmental condition enhance a rainfall occurrence which is effect on agricultural production.
References
[1]
Agboola, A. H., Gabriel, A. J., Aliyu, E. O., & Alese, B. K. (2013). Development of a Fuzzy Logic Based Rainfall Prediction Model. International Journal of Engineering and Technology, 3, 427-435. https://doi.org/10.5751/ES-05896-180422
[2]
Christian, L., Mwongera, C., Camberlin, P., & Micheau, J. B. (2013). Indigenous Past Climate Knowledge as Cultural Built-In Object and Its Accuracy. Ecology and Society, 18, 22-45.
[3]
Cirstea, M. N., Dinu, A., Khor, J. G., & McCormink, M. (2002). Neural and Fuzzy Logic Control of Daves and Power. Oxford: Linacre House.
[4]
Edvin and Yudha (2008). Application of Multivariate ANFIS for Daily Rainfall Prediction: Influences of Training Data Size. Makara, Sains, 12, 7-14.
[5]
Hasan, A., & Rahman, M. A. (2019). A Study on Rainfall Calibration and Estimation at the Northern Part of Bangladesh by Using Mamdani Fuzzy Inference System. Journal of Environment Protection and Sustainable Development, 5, 58-69.
[6]
Hasan, M., Mizutani, M., Goto, A., & Matsui, H. (1995). A Model for Determination of Intake Flow Size Development of Optimum Operational Method for Irrigation Using Fuzzy Set Theory (1). System Nogaku: Journal of the Japanese Agricultural System Society, 11, 1-13.
[7]
Hasan, M., Tsegaye, T., Shi, X., Schaefer G., & Taylor, G. (2008). Model for Predicting Rainfall by Fuzzy Set Theory Using USDA Scan Data. Agricultural Water Management, 95, 1350-1360. https://doi.org/10.1016/j.agwat.2008.07.015
[8]
Hasan, T., & Zenkai, S. (1999). A New Modeling Approach for Predicting the Maximum Daily Temperature from a Time Series. Turkish Journal of Engineering and Environmental Science, 23, 173-180.
[9]
Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing (A Computational Approach to Learning and Machine Intelligence). IEEE Transactions on Automatic Control, 42, 1482-1484.
https://doi.org/10.1109/TAC.1997.633847
[10]
Jim, M. (2005). Application of Fuzzy Logic in Operational Meteorology. Berkeley, CA: Addison Wesley, Longman Inc.
[11]
Jimoh, R. G., Olagunju, M., Folorunso, I. O., & Asiribo, M. A. (2013). Modeling Rainfall Prediction Using Fuzzy Logic. International Journal of Innovative Research in Computer and Communication Engineering, 1, 929-936.
[12]
Kevin, M. P., & Stephen, Y. (1998). Fuzzy Control. Berkeley, CA: Addison Wesley Longman Inc.
[13]
Nasher, N. M. R. (2013). Impact of Climate Variability on Temperature and Rainfall Trend in Bangladesh: A Case of Two Cities. European Journal of Climate Change, 11, 5-10.
[14]
Ross, T. J. (1997). Fuzzy Logic with Engineering Applications (p. 33). New York: McGraw-Hill International Editions.
[15]
Zadeh, L. A. (1965). Fuzzy Sets. Information and Control, 8, 338-353.
https://doi.org/10.1016/S0019-9958(65)90241-X
