Development of a Fuzzy Logic Based Rainfall Prediction Model

The present study investigates the ability of fuzzy rules/logic in modeling rainfall for South Western Nigeria. The developed Fuzzy Logic model is made up of two functional components; the knowledge base and the fuzzy reasoning or decisionmaking unit. Two operations were performed on the Fuzzy Logic model; the fuzzification operation and defuzzification operation. The model predicted outputs were compared with the actual rainfall data. Simulation results reveal that predicted results are in good agreement with measured data. Prediction Error, Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the Prediction Accuracy were calculated, and on the basis of the results obtained, it can be suggested that fuzzy methodology is efficiently capable of handling scattered data. The developed fuzzy rule-based model shows flexibility and ability in modeling an ill-defined relationship between input and output variables.

[1]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[2]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[3]  Brahm P. Verma,et al.  Fuzzy Logic for Biological and Agricultural Systems , 1998, Artificial Intelligence Review.

[4]  E. Chin,et al.  Modeling daily precipitation occurrence process with Markov Chain , 1977 .

[5]  Edvin Aldrian,et al.  APPLICATION OF MULTIVARIATE ANFIS FOR DAILY RAINFALL PREDICTION: INFLUENCES OF TRAINING DATA SIZE , 2007 .

[6]  Fumiaki Fujibe,et al.  Short-term Precipitation Patterns in Central Honshu, Japan Classification with the Fuzzy c-means Met , 1989 .

[7]  R. Welch,et al.  Automated Cloud Classification of Global AVHRR Data Using a Fuzzy Logic Approach , 1997 .

[8]  Bjarne K. Hansen,et al.  FUZZY CASE-BASED PREDICTION OF CLOUD CEILING AND VISIBILITY , 2002 .

[9]  Lucien Duckstein,et al.  Fuzzy rule-based classification of atmospheric circulation patterns , 1995 .

[10]  Kaoru Hirota,et al.  Industrial Applications of Fuzzy Technology , 1993, Springer Japan.

[11]  Shuhei Koyama,et al.  Neuro-Fuzzy Approaches for Modeling the Wet Season Tropical Rainfall , 2006 .

[12]  Tamás D. Gedeon,et al.  Rainfall prediction model using soft computing technique , 2003, Soft Comput..

[13]  Alexander V. Ryzhkov,et al.  Cloud Microphysics Retrieval Using S-Band Dual-Polarization Radar Measurements , 1999 .

[14]  Lucien Duckstein,et al.  Relationship Between Monthly Atmospheric Circulation Patterns and Precipitation: Fuzzy Logic and Regression Approaches , 1996 .

[15]  D. Wilks.  Multisite generalization of a daily stochastic precipitation generation model , 1998 .

[16]  V. Chandrasekar,et al.  Classification of Hydrometeors Based on Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy Systems, and In Situ Verification , 2000 .

[17]  F. Smith.  A MASTER'S THESIS SUBMITTED TO , 2014 .

[18]  J. Shao,et al.  Fuzzy Categorization of Weather Conditions for Thermal Mapping , 2000 .

[19]  T. Wigley,et al.  Application of Markov models to area-average daily precipitation series and interannual variability in seasonal totals , 1993 .

[20]  Geza Pesti,et al.  A fuzzy rule-based approach to drought assessment , 1996 .

[21]  Ajith Abraham,et al.  Will we have a wet summer? Soft computing models for long-term rainfall forecasting , 2001 .

[22]  Lucien Duckstein,et al.  FUZZIFIED EFFECT OF ENSO AND MACROCIRCULATION PATTERNS ON PRECIPITATION: AN ARIZONA CASE STUDY , 1999 .