Machine Learning-Based Algorithms for Weather Forecasting

Abstract

Weather forecast has a big impact on the global economy, accurate and timely
weather forecast is required by all, it affects many aspects of human livelihood
and lifestyle, it also plays a critical role in decision making for severe weather
management and for primary and secondary sectors like agriculture,
transportation, tourism, and industry as they rely on good weather conditions
for production and operations. The erratic and uncertain complex nature of the
weather makes traditional weather forecasting tedious and a challenging task,
traditional weather forecast involves applying technology and scientific
knowledge on Numerical Weather Prediction (NWP), and weather radar to solve
complex mathematical equations to obtain forecasts based on current weather
conditions. These traditional processes utilize expensive, complex physical and
computational power to produce forecasts, which can be inaccurate and have
various catastrophic impacts on society. In this research, a machine learningbased
weather forecasting model was proposed, the model was implemented
using four classifier algorithms which include Random Forest classifier, Decision
Tree Algorithm, Gaussian Naïve Bayes model, Gradient Boosting Classifier, these
algorithms were trained using a publicly available dataset from Kaggle for the
city of Seattle for the period 2012 to 2015. The model’s performance was evaluated;
the Gaussian Naive Bayes algorithm proved to be the best performing algorithm
with a predictive accuracy of 84.153%.