Temperature Prediction using Machine Learning Approaches

Anjali T, Chandini K, Anoop K, Lajish V L
Computational Intelligence and Data Analytics (CIDA Lab)
Department of Computer Science
University of Calicut, India
📝 Paper

Abstract: Weather prediction is one of the most important research areas due to its applicability in real-world problems like meteorology, agricultural studies, etc. We propose a method for temperature prediction using three machine learning models - Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Support Vector Machine (SVM), through a comparative analysis using the weather data collected from Central Kerala during the period 2007 to 2015. The experimental results are evaluated using Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Correlation Coefficients (CC). The error metrics and the CC shows that MLR is a more precise model for temperature prediction than ANN and SVM.

Weather Prediction Dataset: The weather data is procured from 3 different districts in Kerala - Malappuram, Calicut, and Thrissur. Since these districts are situated in the middle region of Kerala, the dataset can efficiently represent the weather conditions and variations of Central Kerala. The samples used for this study are collected from different sources. Daily values of pressure, humidity, wind speed, wind direction and temperature during 2009-2012 are taken from the Agricultural Research Station, Mannuthy, Thrissur. Monthly values of the dry bulb, wet bulb, number of rainy days, rainfall, humidity, and maximum temperature during 2009-2014 are collected from Agricultural Research Station, Anakkayam. Daily hourly data, including humidity, pressure, wind speed and wind direction during 2010 -2015 are collected from Calicut International Airport, Malappuram.
🌏 Dataset Link: https://dcs.uoc.ac.in/cida/resources/wpd.html

Citation

@inproceedings{anjali2019temperature,
  title={Temperature prediction using machine learning approaches},
  author={Anjali, T and Chandini, K and Anoop, K and Lajish, VL},
  booktitle={2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)},
  volume={1},
  pages={1264--1268},
  year={2019},
  organization={IEEE},
  doi={10.1109/ICICICT46008.2019.8993316}
}

Related Publication

Temperature Prediction using Machine Learning Approaches
Anjali T, Chandini K, Anoop K, Lajish VL
Computational Intelligence and Data Analytics (CIDA Lab)
Department of Computer Science
University of Calicut, India
📝 Paper : https://ieeexplore.ieee.org/abstract/document/8993316

For other inquiries, please contact:
Anjali T 📧 anjalithottathil86@gmail.com
Anoop K 📧 anoopk_dcs@uoc.ac.in 🌏 website
Lajish V L 📧 lajish@uoc.ac.in 🌏 website

Acknowledgement
This research is supported by Agricultural Research Station, Anakkayam and College of Horticulture, Kerala Agricultural University, Vellanikkara. We thank Dr. B. Ajith Kumar, Assistant Professor and Head, Department of Agricultural Meteorology, College of Horticulture, Kerala Agricultural University, Vellanikkara, Thrissur and Dr. Abdul Hakkim V M, Professor (Soil and Water Engineering), College of Agriculture, Padannakkad, Kasargode for their assistance in data collection.