Green Sentry - Weather App

Abstract

Accurate hyper local weather forecasting remains one of the most challenging tasks in meteorology due to high spatial variability, changing climate patterns, and limitations of conventional statistical approaches. Traditional forecasting systems largely rely on satellite observations, numerical weather prediction models, and coarse-grained climate datasets, which fail to provide localized predictions required for agriculture, logistics, disaster management, and urban planning. Green Sentry is an AI-powered hyper local weather prediction and alert system designed to overcome these limitations by leveraging machine learning, spatiotemporal analytics, and climate datasets. The system predicts precipitation, classifies weather risks, and triggers alerts in real time. Green Sentry integrates LSTM-based time-series modeling, ensemble machine learning for anomaly detection, GIS-based visual mapping, and a web interface that delivers localized in sights at street-level granularity. Evaluated using datasets from Open-Meteo API, World Bank CCKP, and regional weather logs, the system achieves an MAE of 3.1mm, an RMSE of 5.2 mm, and a heavy rainfall prediction accuracy of 90.1%. Experimental results demonstrate improvements in prediction precision, response time, and contextual climate awareness compared to baseline models. The proposed system supports decision-making in agriculture, flood management, irrigation planning, transportation, and community safety. This paper presents the system architecture, model implementation, evaluation, limitations, and future enhancements toward climate-resilient, AI-driven local forecasting tools.