Logistic regression based next-day rain prediction model

Ogochukwu Ejike, David L. Ndzi, Abdul-Hadi Al-Hassani

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    2 Citations (Scopus)
    86 Downloads (Pure)

    Abstract

    Rain prediction is challenging due to the complex combination of atmospheric factors. This paper presents the application of logistic regression modelling to predict rainfall the next day, using weather parameters from previous days. One year of weather data (temperature, pressure, humidity, sunshine, evaporation, cloud cover, wind direction, and wind speed) from Canberra, Australia, has been used to develop the logistic regression model. The best fit logistic regression models are selected using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Least Absolute Shrinkage and Selection Operator (LASSO) based on feature selection for predictive modelling. These models are evaluated using Area Under the receiver operating characteristics Curve (AUC) and Hosmer-Lemeshow test to determine each model's accuracy and goodness of fit to predict rainfall occurrence the next day. The likelihood of rainfall the next day has been interpreted based on the calculated odds of the selected independent weather parameters. The result shows that using logistic regression (AIC Backward), rainfall the next day can be predicted with 87% accuracy, provided that the appropriate weather parameters are chosen.
    Original languageEnglish
    Title of host publication2021 International Conference on Communication & Information Technology (ICICT)
    Place of PublicationPiscataway, NJ
    PublisherIEEE
    Pages262-267
    Number of pages6
    ISBN (Electronic)9781665439145
    ISBN (Print)9781665439152
    DOIs
    Publication statusPublished - 26 Oct 2021

    Keywords

    • rain
    • rainfall
    • prediction
    • logistic regression

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