International Journal For Multidisciplinary Research

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Prediction of Air Pressure using Support Vector Machine Compare with Multi Layer Perception Model in an Area of North Twenty-four Parganas, West Bengal

Author(s) Rupam Sardar, Sudip Ghosh, Bimal Datta
Country India
Abstract Lifesaving requires the capacity to foresee the weather. Applications for weather prediction in agriculture, transportation, floods, and other natural phenomena are significant. The prediction of Air Pressure is crucial for weather forecasting. Multiple linear regression is one of the techniques available for Air Pressure prediction.
model. To determine how one independent variable and one dependent variable are related to one another In statistics, the linear regression model is an extremely helpful tool. Multiple linear regression is an extension of the linear regression model. We utilized MATLAB to add the variables to our experiment. Weather forecasting relies heavily on Air Pressure prediction. For the purpose of forecasting rainfall, we have chosen Kolkata, or more specifically, the Dum Dum Metropolitan region in North 24 Parganas. We have investigated additional atmospheric forecasting parameters.
applying a multilayer perceptron to predict Air Pressure. Once we get the coefficient, we have the intended and calculated Air Pressure. We have identified the error.
Now we are applying Support Vector Machine over Multi Linear Preceptron to get better results.
Keywords Air Pressure prediction, Multi Layer Perceptron Model, Support Vector Machine,Root Mean Square
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-10
Cite This Prediction of Air Pressure using Support Vector Machine Compare with Multi Layer Perception Model in an Area of North Twenty-four Parganas, West Bengal - Rupam Sardar, Sudip Ghosh, Bimal Datta - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14780
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14780
Short DOI https://doi.org/gtmzt4
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