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Weather Forecasting Using Machine Learning

Mahesh Babar,Shreyash Gujar,Deepak Holkar, Rajveer Dalvi, Ms. Minal Zope

Computer Engineering

AISSMS Institute of Information Technology Pune,India

Abstract— Accurate weather forecasting is crucial for various sectors, including agriculture, disaster management, transportation, and energy. Traditional numerical weather prediction (NWP) models, while effective, are computationally expensive and often struggle with the nonlinear and chaotic nature of atmospheric processes. Machine learning (ML) offers a data-driven approach to enhance forecasting accuracy by identifying complex patterns and relationships within historical weather data.

This research investigates the application of ML algorithms, including regression models, decision trees, support vector machines, and deep learning techniques such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, for weather prediction. We preprocess and analyze large-scale meteorological datasets, extracting relevant features to train and evaluate multiple ML models. Performance metrics such as RMSE, MAE, and R² score are used to assess predictive accuracy.

Our results indicate that deep learning models, particularly LSTMs, outperform traditional ML methods in capturing temporal dependencies and improving forecast precision. This study highlights the potential of ML-driven weather forecasting as a reliable and efficient alternative to conventional approaches, contributing to advancements in meteorology and climate science.

Keywords—— Weather Forecasting, Machine Learning (ML), Deep Learning, Meteorological Data, Numerical Weather Prediction (NWP), Time Series Analysis , Regression Models , Decision Trees , Support Vector Machines (SVM), Artificial Neural Networks (ANN), Feature Engineering ,Predictive Modeling, Big Data in Meteorology ,Climate Prediction , Temperature Forecasting, Rainfall Prediction ,Extreme Weather Events , Statistical Forecasting.