Sahay - Flood & Rainfall Intelligence: a Python web app using 1D‑CNN deep learning and ARIMA time‑series forecasting for monthly precipitation prediction and flood-risk classification. Developed for the DSN2099 Project Exhibition - II @ VIT

Regional precipitation forecasting in Israel using machine learning

ML pipeline to forecast Mumbai's monthly rainfall using 121 years of IMD data. Compares XGBoost, Random Forest, SARIMA, LSTM, and Prophet — XGBoost achieves a ~69% RMSE reduction over SARIMA. Includes 12-month forward forecast and trained model.

End-to-end time series forecasting project focused on predicting annual rainfall trends and identifying peak rainfall periods using historical meteorological data.

Deep Learning based Climate Time-Series Forecasting using LSTM Networks and PyTorch.