International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 05 | May 2025
p-ISSN: 2395-0072
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SeasonSense : Weather Alert and Prediction System Manasi Mishra1, Priya Srivastava2, Deeya Upadhyay3 , Sonali Bodekar4 1Student, Dept. of Information Technology, Usha Mittal Institute Of Technology, Maharashtra, India 2Student, Dept. of Information Technology, Usha Mittal Institute Of Technology, Maharashtra, India 3Student, Dept. of Information Technology, Usha Mittal Institute Of Technology, Maharashtra, India
4Professor, Dept. of Information Technology, Usha Mittal Institute Of Technology, Maharashtra, India
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Abstract - SeasonSense is an intelligent weather
climatic conditions using key evaluation metrics like accuracy, precision, recall, and F1-score [8].
forecasting and alert platform that utilizes advanced machine learning algorithms to analyze both historical and real-time meteorological data for accurate prediction of key weather conditions, including rainfall, temperature fluctuations, and severe events such as storms. Aimed at enhancing public safety and preparedness, the system delivers precise, location-specific forecasts and timely alerts. SeasonSense employs adaptive learning methodologies to continuously improve its predictive accuracy over time, refining its models based on new data patterns. The platform is designed with a user-friendly interface that provides seamless access to real-time alerts, weather forecasts, and historical climate trends, making it accessible to a wide range of users. Its versatility supports critical decision-making across various sectors such as agriculture, transportation, disaster management, and urban planning—helping stakeholders mitigate weather-related risks through informed planning and responsive actions. By offering reliable, real-time insights, SeasonSense significantly contributes to the evolution of weather prediction technologies and sets a foundation for future innovations in climate monitoring and early warning systems.
The system integrates a real-time alert mechanism, supported by continuous data ingestion pipelines that enable seamless updates and reliable weather predictions [14]. It provides in-depth insights into weather patterns, including rainfall intensity, temperature shifts, and extreme events like storms [9]. One of SeasonSense’s key strengths is its adaptability to diverse environmental conditions, positioning it as a valuable tool in modern meteorological forecasting [7]. By harnessing AI-driven predictive analytics, it facilitates informed decision-making in crucial domains such as agriculture, disaster management, and urban infrastructure planning [5]. SeasonSense represents a significant advancement in AI-powered weather forecasting, contributing to a future of more intelligent and precise meteorological systems [6][10].
1.1 Problem Statement Unstable weather patterns and severe climatic events pose growing risks to agriculture, infrastructure, and public safety. Conventional forecasting methods often struggle with regional precision and fail to deliver timely warnings, increasing threats to lives and property.
Key Words: Weather forecasting, climate prediction, machine learning, meteorological analysis, weather alerts, disaster management, public safety, predictive analytics.
SeasonSense: Weather Alert and Prediction System tackles these issues by utilizing cutting-edge machine learning techniques to improve weather predictions and real-time alert systems. By processing both historical and live meteorological data, it empowers proactive disaster preparedness and risk mitigation.
1.INTRODUCTION SeasonSense is an intelligent weather forecasting system that utilizes cutting-edge machine learning models, including CatBoost, XGBoost [2], Random Forest [3], and Support Vector Classifier [3]. By analyzing extensive realtime and historical meteorological data, the system delivers precise weather forecasts and timely alerts [1].
1.2 Objective SeasonSense: Weather Alert and Prediction System is a machine learning-powered web platform designed to improve the accuracy of weather forecasting and deliver real-time, location-specific alerts for extreme weather conditions. By integrating historical weather datasets with live data from the Open-Meteo API, the system enhances public preparedness, reduces the impact of adverse weather on agriculture, transportation, and daily life, and strengthens early warning capabilities. This project
To enhance predictive accuracy, SeasonSense implements advanced data preprocessing techniques such as feature selection, data normalization, and anomaly detection [4]. These steps ensure that machine learning models are trained on refined, high-quality datasets. The performance of each algorithm is rigorously assessed under varying
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