Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters

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Karthika. B. S 1,* Paresh Chandra Deka 1

1. National Institute of Technology Karnataka, Surathkal, 575025, India

* Corresponding author.


Received: 29 Apr. 2015 / Revised: 1 Aug. 2015 / Accepted: 25 Sep. 2015 / Published: 8 Jan. 2016

Index Terms

Adaptive Neuro Fuzzy Inference system, Wavelet, Hybridization, Temperature, Modeling


The precise modeling of average air temperature is a significant and much essential parameter in frame of reference for decision-making in agriculture field, drought detection and environmental related issues. The aim of this research is to construct an accurate model to modeling average air temperature using hybrid Wavelet-ANFIS techniques. Being cognizant of the fact, uncertainty handling capability is achieved with ANFIS technique; a cognitive approach to integrate ANFIS technique along with pre-processed data by using Wavelet transformation. Detailing on approach, in this work utilized Discrete Wavelet transform under Daubechies mother Wavelet up to 3rd level of decomposition. This study extends up to seven station’s meteorological data records. The following developed hybrid model’s performance is compared with single ANFIS models for all seven stations. The obtained results were evaluated using correlation coefficient, root mean square error and scatter index These results confirmed that the proposed hybridized Wavelet- ANFIS model has estimable potential in terms of modeling temperature than ANFIS model alone.

Cite This Paper

Karthika. B. S, Paresh Chandra Deka,  "Modeling of Air Temperature using ANFIS by Wavelet Refined Parameters", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.1, pp.25-34, 2016. DOI:10.5815/ijisa.2016.01.04


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