Comparative Evaluation of Weather Forecasting using Machine Learning Models

TL;DR

This paper compares various machine learning algorithms for weather forecasting, specifically predicting precipitation and temperature, using a 20-year dataset from Dhaka city to evaluate their performance and insights.

Contribution

It provides a comparative analysis of multiple machine learning models for weather prediction, highlighting their relative effectiveness and feature correlations.

Findings

Gradient Boosting and Neural Networks performed best.

Stacking models showed improved accuracy over individual algorithms.

Insights into feature importance for weather prediction.

Abstract

Gaining a deeper understanding of weather and being able to predict its future conduct have always been considered important endeavors for the growth of our society. This research paper explores the advancements in understanding and predicting nature's behavior, particularly in the context of weather forecasting, through the application of machine learning algorithms. By leveraging the power of machine learning, data mining, and data analysis techniques, significant progress has been made in this field. This study focuses on analyzing the contributions of various machine learning algorithms in predicting precipitation and temperature patterns using a 20-year dataset from a single weather station in Dhaka city. Algorithms such as Gradient Boosting, AdaBoosting, Artificial Neural Network, Stacking Random Forest, Stacking Neural Network, and Stacking KNN are evaluated and compared based on their performance metrics, including Confusion matrix measurements. The findings highlight remarkable achievements and provide valuable insights into their performances and features correlation.