Type - 2 mamdani fuzzy inference system based model for rainfall forecasting

Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rain...

Full description

Saved in:
Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 46; no. 2; p. 4791
Main Authors R Syed Aamir Adnan, Kumaravel, R
Format Journal Article
LanguageEnglish
Published London Sage Publications Ltd 01.01.2024
Subjects
Correlation coefficients
Fuzzy logic
Inference
Mathematical models
Performance evaluation
Performance measurement
Rainfall
Root-mean-square errors
Weather forecasting
Online AccessGet full text
ISSN1064-1246
1875-8967
DOI10.3233/JIFS-235828

Cover

More Information
Summary:Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R2) and Root Mean-Squared Error (RMSE) have been used to evaluate the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-235828