Real Time Prediction of Temperature using ANFIS-SUGENO Model
Rashmi Bhardwaj1, Varsha Duhoon2
1Rashmi Bhardwaj*, Professor of Mathematics, Research Lab, Guru Gobind Singh Indraprastha University, Delhi, India.
2Varsha Duhoon, Research Scholar, USBAS, GGS Indraprastha University, Delhi, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 461-469 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9555109119/2019©BEIESP | DOI: 10.35940/ijeat.A9555.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Temperature patterns are continuously change over time and but dependences on the temperature of most of the industries has not yet changed hence making it important for the scientists to predict temperature on a regular basis as most share of the industries in GDP of the economy of country has its dependence on the weather and hence the average of the weather patterns known as climate. In relation to this, the need is to generate a system which can foretell the temperature so that it can help in the various policy making and foreseeing the upcoming catastrophic event. Adaptive Neuro Fuzzy Inference System (ANFIS) and SUGENO model a tools & techniques under Artificial Intelligence used for analyzing the data set and foretell the behavior for upcoming reference. ANFIS-SUGENO model used to analyses weather parameters like Humidity, Maximum & Minimum temperature, speed of wind, Bright sunshine (BSS), Evaporation for Delhi daily data set from January 1, 2017 up till February 28, 2018 and further from January 1, 2017 up till November 30, 2018 is used for foretelling and it is observed that the observed and predicted values are much related.
Keywords: Artificial Intelligence, Adaptive Neuro Fuzzy Inference System (ANFIS), SUGENO Model, Fuzzy Logic, Non-linear.