Affiliation:
1. Amrutvahini College of Engineering, Sangamner, Maharashtra, India
Abstract
Weather forecasting with traditional technique is mainly done by physical model, still in many parts of the world. Though not neglecting the importance of the model there is an alternative method where recorded data of past can be used for predicting the future weather data. The predicted data may not be exact, but less time consuming and more efficient. Minimum temperature, maximum temperature, average temperature, precipitation percentage these are the common parameters and predicting these with another less resource-based method with some precision will help us going. Machine learning can be used for processing the data based on models like linear regression, functional regression, circular, statistical which processes the data and reduces the error. On comparing the result of model based on different location one can used a model based on requirement. The result obtained then can be analyzed and further improved upon input variables and data size.
Reference17 articles.
1. Bureau of Meteorology. The “Federation Drought”, 1895–1902. 2009. Available online:https://webarchive.nla. gov.au/awa/20090330051442/http://pandora.nla.gov.au/pan/96122/200903171643/www.bom.gov.au/lam/climate/levelthree/c20thc/drought1.html (accessed on 4 June 2020).
2. Mohammad Bannayan and Gerrit Hoogenboom, "Weather analogue: A tool for real-time prediction of daily weather data realizations based on a modified knearest neighbor approach", Environmental Modelling & Software, vol. 23, no. 6, pp. 703-713, 2008.
3. E. Penabad, I. Alvarez, C.F. Balseiro, M. deCastro, B. Gómez,V. Pérez-Muñuzuri and M. Gómez-Gesteira, "Comparative analysis between operational weather AVCOE 5 prediction models and QuikSCAT wind data near the Galician coast", Journal of Marine Systems, vol. 72, no. 14, pp. 256-270, 2008.
4. D. Pelleg, A. Moore (2000): “X-means: Extending K-means with Efficient Estimation of the Number of Clusters”; ICML ’00 Proceedings of the Seventeenth International Conference on Machine Learning, pp. 727-734.
5. BMP180 Barometric Pressure Sensor Module. [Online]. Available: https://ae01.alicdn.com/kf/HTB108MyX2JNT KJjSspoq6A6mpXai/BMP180-GY-68-GY68-3-3V-5V-BMP-180-Temperature-Pressure-Sensor-Module-Barometric-IICI2C.jpg