Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting | Zendy

M.Shochibul Burhan | Zendy; Wayan Firdaus Mahmudy | Zendy; Rizdania Dermawi | Zendy

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Sugeno-Type Fuzzy Inference Optimization With Firefly and K-Means Clustering Algorithms For Rainfall Forecasting

Author(s) -

M.Shochibul Burhan,

Wayan Firdaus Mahmudy,

Rizdania Dermawi

Publication year - 2018

Publication title -

journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2540-9824

pISSN - 2540-9433

DOI - 10.25126/jitecs.20183134

Subject(s) - firefly algorithm , cluster analysis , fuzzy inference , k means clustering , fuzzy logic , data mining , algorithm , computer science , fuzzy clustering , mathematics , artificial intelligence , fuzzy control system , adaptive neuro fuzzy inference system , particle swarm optimization

Rainfall is very influential in our daily lives, ranging from agriculture, aviation, to flood-prone areas. The intensity of rainfall is used as an early detection for preventing harmful effects of rainfall. This research used Sugeno-Method Fuzzy Logic, in which the prediction is accomplished by mapping rules from the data obtained using the K-Means Clustering Algorithm as the classification to form the membership function and mapping rules and Firefly Alghorithm for optimization output. The test result from the 30 examined data found is 2.93 RMSE. This shows that the data support from K-Means Clustering and Firefly Algorithms of the fuzzy logic can predict precipitation accurately.

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