Python framework for short-term ensemble prediction systems.

CIKM contest entry 'Convolutional LSTM neural network to extrapolate radar images, and predict rainfall'

a rainfall animation for unix CLI

Modular Assessment of Rainfall-Runoff Models Toolbox - Matlab code for 47 conceptual hydrologic models

This study explores the influence of dam management practices and rainfall variability on flood patterns in the Niger Delta.

This project analyzes and visualizes rainfall data for Nigeria from 2018 to 2024

CIKM AnalytiCup 2017 is an open competition that is sponsored by Shenzhen Meteorological Bureau, Alibaba Group and CIKM2017. Our team got the third place in the first phrase. And in the second phrase we got the fourth place.

Rainfall-Runoff modelling playground

Custom Home Assistant card displaying a meteogram with wind barbs and rainfall.

ApnaAnaaj aims to solve crop value prediction problem in an efficient way to ensure the guaranteed benefits to the poor farmers. The team decided to use Machine Learning techniques on various data to came out with better solution. This solution uses Decision Tree Regression technique to predict the crop value using the data trained from authentic datasets of Annual Rainfall, WPI Index for about the previous 10 years. This implementation proved to be promising with 93-95% accuracy.

This project helps to predict the suitable crop and its price according to the air humidity, soil moisture, soil pH and rainfall.

A python package to download and analyze the Global Precipitation Measurement Mission (GPM) data archive

Example notebook, showing how to use LSTMs for rainfall-runoff modeling

A beginner's guide to carry out extreme value analysis, which consists of basic steps, multiple distribution fitting, confidential intervals, IDF/DDF, and a simple application of IDF information for roof drainage design. The guide mainly focuses on extreme rainfall analysis. However, the basic steps are also suitable for other climatic or hydrologic variables such as temperature, wind speed or runoff.

Highly vulnerable to seasonal flooding due to its low-lying terrain, heavy rainfall, and proximity to major water bodies. Flood mapping and monitoring in the state involve the use of remote sensing, Geographic Information Systems (GIS), and hydrological modeling to assess flood-prone areas, predict flood events, and guide disaster response effort

Advance warning system for flood with rainfall analysis

Outdoor Weather Station with Wind Speed, direction &Rainfall sensors

Integrate terrain, rainfall, soil, and vegetation indices to predict landslide-prone regions and support disaster risk reduction planning.

A synthetic machine learning pipeline for forecasting agricultural yields under climate change. It integrates climate, soil, management, and remote-sensing data, trains ensemble models, evaluates performance, and runs scenario analysis (+2 °C, −10% rainfall, +30 ppm CO₂) to assess future food security risks.

This project implements a basic Rainfall-Runoff Modeling framework using the SCS Curve Number (CN) Method to assess flood risk based on daily rainfall data.

A machine learning model in python that recommends the best crop to grow based on Soil composition, Ph level, rainfall and geographical location.

Repository for article "Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting"

☀️🌧 Yet another weather app for Singapore

Heavy rainfall intensity as a function of duration and return period is defined according to DWA-A 531 (2012). This program reads rainfall measurement data and calculates the distribution of design rainfall as a function of both return period and duration, for durations up to 12 hours (and beyond) and return periods in the range 0.5 a ≤ Tₙ ≤ 100 a.

Use of time series modelling tools including ARIMA, LSTM, and Monte Carlo simulation to model electricity consumption, rainfall and temperature data.

This project compares Kriging and Random Forest machine learning for spatial interpolation of rainfall using synthetic data in Google Earth Engine. It generates >100 random rainfall points, interpolates across a region, and visualizes differences between geostatistical and ML-based predictions.

Forest Fire Risk Prediction model using a machine learning approach (Random Forest Classifier) trained on environmental data (temperature, humidity, wind, rainfall, etc.). This example includes data preprocessing, model training, evaluation, and prediction.

Prediction of whether a flood may occur or not, depending rainfall data, using machine learning algorithms

GR4J rainfall runoff model implemented in Python

A-Frame Rainfall effect component