Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)

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Mamdani Fuzzy Based Prediction of Rainfall Fuzzy Rainfall Index

Authors
Ayyakkannu Selvaraj1, *, Anakath Arasan2, Sharvari Tamane1, Rajendiran Kannadasan3, Subbarayan Saravanan4, Mayur K. Jadhav1, Ansari Mohammed Mohsin1
1University Department of Information and Communication Technology, MGM University, Aurangabad, Maharashtra, India
2Department of Information Technology, EGS Pillay Engineering College, Nagaptinam, Tamil Nadu, India
3School of Computer Science Engineering, VIT University, Vellore, Tamil Nadu, India
4Department of Civil Engineering, National Insitute of Technology, Trichy, Tamil Nadu, India
*Corresponding author. Email: aselvaraj@mgmu.ac.in
Corresponding Author
Ayyakkannu Selvaraj
Available Online 1 May 2023.
DOI
10.2991/978-94-6463-136-4_74How to use a DOI?
Keywords
Fuzzy rainfall index; Fuzzification; Defuzzification; If-then rules; Surface plot; Regression
Abstract

This study attempts to investigate the link between rainfall and climatic large-scale synoptic patterns. Here, we employed the fuzzy inference system (FIS) to forecast rainfall. Six linguistic variables were considered as an input for the proposed system, split into two FIS. Average Mean Temperature (degree celsius), Mean Wind velocity (KM-PH), and Evapotranspiration (mm/day) belong to FIS1. In contrast, Relative humidity (%), Season, and Mean sun shines (hrs/day) belong to FIS2, and each model has three triangular membership functions, excluding the season variable. It has four bi-membership functions: summer, winter, South West, and North East. The output models have four memberships function Very high, High, Normal, Low, and Very Low for FIS1 and FIS2; respectively. The IF-THEN rules were assigned based on the individual significance of linguistic variables for rainfall and from expert opinion for the model FIS1, FIS2 and FRFI, In this article, we had implemented 72 numbers of possible rules, moreover model results can be reviewed into surface 3D-Plot. The implication of each variable with output such as FIS1, FIS2, and FRFI was addressed. Finally, predicted values were compared with the actual rainfall data. Thus, the proposed model would be expected rainfall at a reasonable accuracy. All the implementation has been done in MATLAB7 Fuzzy toolbox.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
Series
Advances in Computer Science Research
Publication Date
1 May 2023
ISBN
10.2991/978-94-6463-136-4_74
ISSN
2352-538X
DOI
10.2991/978-94-6463-136-4_74How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ayyakkannu Selvaraj
AU  - Anakath Arasan
AU  - Sharvari Tamane
AU  - Rajendiran Kannadasan
AU  - Subbarayan Saravanan
AU  - Mayur K. Jadhav
AU  - Ansari Mohammed Mohsin
PY  - 2023
DA  - 2023/05/01
TI  - Mamdani Fuzzy Based Prediction of Rainfall Fuzzy Rainfall Index
BT  - Proceedings of the International Conference on Applications of Machine Intelligence and Data Analytics (ICAMIDA 2022)
PB  - Atlantis Press
SP  - 838
EP  - 850
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-136-4_74
DO  - 10.2991/978-94-6463-136-4_74
ID  - Selvaraj2023
ER  -