[01] Ahammodullah Hasan, Department of Mathematics, Faculty of Science, Islamic University, Kushtia, Bangladesh. [02] Mohammad Anisur Rahman, Department of Mathematics, Faculty of Science, Islamic University, Kushtia, Bangladesh.

In this paper, the Mamdani fuzzy inference system has been used to estimate the average rainfall behaviour in the north part of Bangladesh. The authorized rainfall data of Bangladesh Meteorological Department were calibrated for better estimation. To perform the fuzzy inference system, knowledge base and the fuzzy reasoning or decision making functional components were used. The mⁿ rules of fuzzy-logic principles were used to make operations for both the cases of fuzzification operation and defuzzification operation. The triangular membership functions were used for three major parameters (Temperature, Humidity, Wind speed) of Environment. In addition, the input and output variables were initially partitioned into five linguistic ranges Very high, High, Medium, Low, and Very Low. The developed system was then applied to the calibrated data to find the actual estimation. In this study, we have found the observed data and the estimated result makes a good agreement. The developed fuzzy rule-based model shows flexibility and ability in modelling an ill-defined relationship between input and output variables. The model estimation error was found below the 5% significance level. The RMS value of the estimated result was 0.51%. The study of fuzzy logic based rainfall prediction method by using the Mamdani fuzzy inference system may be successively used for different environmental problem estimation to mitigate unexpected meteorological problems.

Fuzzy Logic, Membership Function, Rainfall Estimation, Rules-Based, FIS, Fuzzy Levels, Fuzzification, Defuzzification

[01] Agboola, A. H., A. J. Gabriel, E. O. Aliyu and B. K. Alese, (2013), “Development of a Fuzzy Logic Based Rainfall Prediction Model”, International Journal of Engineering and Technology, Vol. 3, No. 4, pp. 427-435. [02] Asklany, S. A., Elhelow, K., Youssef, I., Abd El-wahab, M., (2011). “ Rainfall events prediction using rule-based fuzzy inference system”, Atmospheric Research, 101 (1), pp. 228–236. [03] Abraham, A., Philip N. and Joseph B. (2001); “Soft Computing Models for Long Term Rainfall Forecasting”: In: 15th European Simulation Multi conference (ESM, August/September 2001), Modeling and Simulation 2000, Kerckhoffs, E. J. H. and M. Snorek (Eds.). Czech Republic, Prague, pp: 1044-1048. [04] Center, B. and Verma B. P. (1998); “Fuzzy logic for biological and agricultural systems”, Artificial Intelligence Reviee, Vol. 12, Issue 1-3, pp. 213-225. [05] Edvin and Yudha, (2008), “Application of Multivariate ANFIS for Daily Rainfall Prediction Influence of Training Data Size”, Makara, Sains, Vol. 12, No. 1, pp. 7-14 7. [06] Ghalhary, G. A. F., M. M. Baygi and M. H. Nokhandan, (2009), “Annual Rainfall Forecasting by Using Mamdani Fuzzy Inference System”, Research Journal of Environment Science, Vol. 3, No. 4, pp. 400-413. [07] Guhathakurta, P., (2006), “Long-range Monsoon Rainfall Prediction of 2005 for the Districts and Sub Division Kerala With Artificial Neural Network”, Current Science 90, pp. 773-779. [08] Halide, H. and Riddp., (2002), “Modeling inter-annual variation of a local rainfall data using a fuzzy logic technique”, Proceedings of International Forum on Climate Prediction, James Cook Universiy, Australia, pp. 166-170. [09] Hasan, M., X. F. Shi, T. Tsegaye, N. U. Ahmed and S. M. M. Khan, (2013), “Rainfall Prediction Model Improvement by Fuzzy Set Theory”, Journal of Water Resource and Protection, Vol. 5, No. 1, pp. 1-11. [10] Indrabayu, N. Harun, M. S. Pallu and A. Achmad, (2013), “Statistic Approach versus Artificial. [11] Intelligence for Rainfall Prediction Based on Data Series”, International Journal of Engineering and Technology, Vol. 5, No. 2, pp. 1962-1969. [12] Karamouz, M., B. Zahraie and S. Eghdamirad, (2004), “Seasonal Rainfall Forecasting using Meterological Signals”, Proceeding of the 1st Conference of Iran Water Sources Management, Techonological Faculty, Tehran University, Issue Nov. 15-16, pp. 60-70. [13] Mousavi, B. M., G. Makhsud, G. A. F. Ghalhari and M. H Nokhandan, (2008), “Assessment of the relation between the large scale climatic signals with rainfall in the khorasan. J. agric. Natur. Resour., Issue -15, PP. 217-224. [14] Özelkan, E. C., F. Ni and L. Duckstein, (1996), “Relationship between monthly atmospheric circulation patterns and precipitation”, Fuzzy logic and regression approaches, Water Resour. Res., 32, pp. 2097-2103. [15] Suwardi, A., Takenori K. and Shuhei K. (2006); “Neuro-fuzzy approaches for modeling the wet season tropical rainfall”, Agricultural Information Research, Vol. 15, No. 3, pp. 331-334. [16] Zadeh, L. A. Zadeh, K. Tanaka and M. Shimura, (1975), “Calculus of fuzzy restrictions, Fuzzy Sets and their Applications to Cognitive and Decision Processes”, Academic Press, New York, pp. 1-39. [17] Wong, K. W., P. M. Wong, T. D. Gedeon and C. C. Fung, (2003), “Rainfall Predication Model Using Soft Computing Technique”, Soft Compute, Fusion Found at Methodology, Vol. 7, Issue 6, pp. 434-438. [18] Liong, S., M. K. D. Quinzio, G. Fleming, M. Permezel, G. E. Rice and H. M. Georgiou, (2013), “Prediction of spontaneous preterm labour in at-risk pregnant women”, An official journal of Society for Reproduction and Family, Vol. 146, Issue. 4, pp. 335-345. [19] Liu, H. and V. Chandrasekhar, (2000), “Classification of hydrometeors based on polar metric radar measurements: Development of fuzzy logic and neuro-fuzzy systems and in situ verification”, J. Atmos. Oceanic Technol., 17: 140-164. [20] Lukasiewicz, J., (1920), “Studies in Logic and the Foundations of Mathematics”, North Holland, Amsterdam Issue 5, pp. 170–171 [21] Shahi, A, R. Atan, M. N. Sulaiman, (2009), “AN EFFECTIVE FUZZY C-MEAN AND TYPE-2 FUZZY LOGIC FOR WEATHER FORECASTING”, Journal of Theoretical and Applied Information Technology, Vol. 5, No. 5, pp. 556-567.