Artificial intelligence is used in a smart city to understand the emotional pulse of its residents

Authors

  • M. Praveena, K. Sirisha , G. Siva Prasad , Dr. D Jebakumar immanuel, U. Sai Bhavya

DOI:

https://doi.org/10.17762/msea.v69i1.1585

Abstract

Abstract: India, which is in the tropical wet and dry area, receives a huge amount of precipitation each year, with the monsoon season being the main contributor. The environment resulting challenges also has led to significant improvements in the average rain distribution and its volatility, as well as the severity and frequency of severe rainfalls. Rain also exhibits great temporal and geographic volatility. Different Time-series prognostication models for predicting rain, including Holt's Linear Trend methodology (HLTM), Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), Holt's Winter Seasonal Methodology (HWSM), and seasonal Autoregressive Integrated Moving Average, were used in this study (SARIMA). In order to analyse the most basic time-series prognostication model, a comparison analysis was built. The HWSM model was determined to have the lowest error rate when compared to the other models. Mean square error (MSE), Root Mean square error (RMSE), and Mean Absolute Error are the analysis criteria used to compare different time-series rain forecast methods (MAE). The HWSM model exhibited, by far, the lowest error rate of the other models, with error rates of 4.767 and 4.343 RMSE, 1.51 and 1.432 MAE, and 25.65 and 24.75 MSE for datasets 1 and 2, respectively.

Downloads

Published

2023-01-12

How to Cite

M. Praveena, K. Sirisha , G. Siva Prasad , Dr. D Jebakumar immanuel, U. Sai Bhavya. (2023). Artificial intelligence is used in a smart city to understand the emotional pulse of its residents. Mathematical Statistician and Engineering Applications, 69(1), 50–60. https://doi.org/10.17762/msea.v69i1.1585

Issue

Section

Articles