Machine Learning-Based Intelligent Weather Monitoring and Predicting System

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-4
Year of Publication : 2022
Authors : Semere Gebretinsae, Mohd Wazih Ahmad, T.Gopi Krishna, Nune Sreenivas, Biruk Yirga
  10.14445/22315381/IJETT-V70I4P213

MLA 

MLA Style: Semere Gebretinsae, et al.  "Machine Learning-Based Intelligent Weather Monitoring and Predicting System ." International Journal of Engineering Trends and Technology, vol. 70, no. 4, Apr. 2022, pp. 152-163. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I4P213

APA Style: Semere Gebretinsae, Mohd Wazih Ahmad, T.Gopi Krishna, Nune Sreenivas, Biruk Yirga. (2022). Machine Learning-Based Intelligent Weather Monitoring and Predicting System . International Journal of Engineering Trends and Technology, 70(4), 152-163. https://doi.org/10.14445/22315381/IJETT-V70I4P213

Abstract
The problem of weather prediction for the agricultural domain is of prime importance for the agriculture experts, farmers and research institutions across Ethiopia. This research proffered time series and machine learning modelling techniques for the design, development and implementation of lightweight, easy deploy models for the meteorology centers and researchers in the field of weather forecasting for Ethiopia. The team proposed Machine learning-based weather prediction models as an alternative way of doing this task. The proposed Machine learning models work on the principle of learning the patterns in the observed data from the recent past. Totally five important weather parameters named Temperature, Precipitation, Sunshine Hours, Relative Humidity and Rainfall were selected for this research in the Adama region. The comparative results and accuracy in the prediction of shortage of resources and simple script based execution of prediction tasks have encouraged meteorology personnel to learn and use techniques proposed in this research. The data was collected from 44 meteorology stations in the region. Past ten years, data for 33 variables were obtained from the Adama Meteorology centre and Addis Ababa meteorology centre.

Keywords
Weather prediction, Machine learning, NWP, EMA, ARIMA, AR-ANN.

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