87 skills found · Page 1 of 3
KartoffelToby / Better ThermostatThis custom component for Home Assistant will add crucial features to your climate-controlling TRV (Thermostatic Radiator Valves) to save you the work of creating automations to make it smart. It combines a room-temperature sensor, window/door sensors, weather forecasts, or an ambient temperature probe to decide when it should call for heat and automatically calibrate your TRVs to fix the imprecise measurements taken in the radiator's vicinity.
pangeo-data / Climpred:earth_americas: Verification of weather and climate forecasts :earth_africa:
Aalto-QuML / ClimODEClimODE: Climate and Weather Forecasting With Physics-informed Neural ODEs
jeonghwan723 / DL ENSOCNN for climate forecast
djlampert / PyHSPFPython extensions to the Hydrological Simulation Program in Fortran (HSPF), including classes for gathering input data, building input files, performing simulations, postprocessing results, calibrating hydrology process parameters, and forecasting climate and land use change effects on water resources
MTry / Homebridge Smart IrrigationTurn any electrical irrigation valve into a smart-valve.. or run your pumps on a smart schedule based on your climate! This homebridge plugin exposes a multi-zone irrigation sprinkler dummy control system to Apple's HomeKit. Although a dummy, it brings smarts of an evapotranspiration based climate and plant adaptive irrigation controller with the use of OpenWeatherMap API. The plugin can optionally email you, and/or send you push notifications through Pushover or Pushcut, with the watering schedule it has calculated, or when a watering run is completed, along with the next 7-day weather forecast. Added option to expose system controls to Homekit allowing a user to enable/disable irrigation, rechecks, push and email notifications from within the Home App. Associated WaterLevel Characteristic shows the % of watering cycle remaining.
SciTools / Cf UnitsUnits of measure as required by the Climate and Forecast (CF) Metadata Conventions
Dogiye12 / Agricultural Yield Forecasting Under Climate ChangeA 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.
Marticles / Airnet☁Air pollution visualization and forecasting platform based on Spring Cloud(基于Spring Cloud的环境污染物数据分析与预测平台)
theashishgavade / AgroWorldappThe farming app tracks climate data, forecasts, and connects farmers with affordable seed suppliers and transporters. It facilitates communication with vehicle owners and offers information on crops, diseases, and skill development. Users can access live farming news and utilize a Task Manager for reminders. This app available in Hindi and English.
WUR-AI / AgML CY BenchCY-Bench (Crop Yield Benchmark) is a comprehensive dataset and benchmark to forecast crop yields at subnational level. CY-Bench standardizes selection, processing and spatio-temporal harmonization of public subnational yield statistics with relevant predictors. Contributors include agronomers, climate scientists and machine learning researchers.
Geraldine-Winston / Building Energy Consumption Forecasting Under Climate Variability Using LSTM.This project forecasts building energy consumption using LSTM models, incorporating climate variables like temperature and humidity. It enhances prediction accuracy under seasonal and daily variability for improved energy management and planning.
Geraldine-Winston / Air Quality Prediction Under Changing Climate Using Deep Ensemble Models.This project predicts PM2.5 air quality levels under changing climate conditions using a deep ensemble of neural networks, improving prediction robustness and aiding policymakers with reliable forecasts for environmental planning and intervention.
Mjrovai / MJRoBot Home Weather StationHome Weather Station, with outdoor information as temperature and climate conditions including the present day and a 3 days forecast. Also indoor information as temperature and air humidity.
kayhendriksen / FoehnDownload MeteoSwiss Open Government Data — weather stations, radar, hail, forecasts and climate series — via Python API, CLI, or MCP server, as DataFrames or Parquet files
salvaRC / El GNNinoCode associated with the NeurIPS 2020 Climate Change workshop proposal paper "Graph Neural Networks for Improved El Niño Forecasting"
semvijverberg / RGCPDClimate analysis toolbox to investigate teleconnections, test for causality, and make forecasts.
vcerqueira / Tsa4climateTackling Climate Change with Time Series Analysis and Forecasting
Geraldine-Winston / Crop Yield Prediction Under Climate Change Scenarios Using Ensemble ML.This project uses ensemble machine learning techniques to predict crop yield based on climate change scenarios. The models integrate climatic variables such as temperature, rainfall, CO₂ levels, and soil properties to forecast agricultural output.
shishirdas / Rain Fall Data Analysis Using Data ScienceContext Rainfall is very crucial things for any types of agricultural task. Climate related data is important to analyse agricultural and crop seeding related field, where those data can be used to show the predict the rainfall in different season also for different types of crops. Developed application can be found from http://ml.bigalogy.com/ Paper: http://dspace.uiu.ac.bd/handle/52243/178 Abstract Mankind have been attempting to predict the weather from prehistory. For good reason for knowing when to plant crops, when to build and when to prepare for drought and flood. In a nation such as Bangladesh being able to predict the weather, especially rainfall has never been so vitally important. The proposed research work pursues to produce prediction model on rainfall using the machine learning algorithms. The base data for this work has been collected from Bangladesh Meteorological Department. It is mainly focused on the development of models for long term rainfall prediction of Bangladesh divisions and districts (Weather Stations). Rainfall prediction is very important for the Bangladesh economy and day to day life. Scarcity or heavy - both rainfall effects rural and urban life to a great extent with the changing pattern of the climate. Unusual rainfall and long lasting rainy season is a great factor to take account into. We want to see whether too much unusual behavior is taking place another pattern resulting new clamatorial description. As agriculture is dependent on rain and heavy rainfall caused flood frequently leading to great loss to crops, rainfall is a very complex phenomenon which is dependent on various atmospheric, oceanic and geographical parameters. The relationship between these parameters and rainfall is unstable. Beside this changing behavior of clamatorial facts making the existing meteorological forecasting less usable to the users. Initially linear regression models were developed for monthly rainfall prediction of station and national level as per day month year. Here humidity, temperatures & wind parameters are used as predictors. The study is further extended by developing another popular regression analysis algorithm named Random Forest Regression. After then, few other classification algorithms have been used for model building, training and prediction. Those are Naive Bayes Classification, Decision Tree Classification (Entropy and Gini) and Random Forest Classification. In all model building and training predictor parameters were Station, Year, Month and Day. As the effect of rainfall affecting parameters is embedded in rainfall, rainfall was the label or dependent variable in these models. The developed and trained model is capable of predicting rainfall in advance for a month of a given year for a given area (for area we used here are the stations (weather parameters values are measured by Bangladesh Meteorological Department). The accuracy of rainfall estimation is above 65%. Accuracy percentage varies from algorithm to algorithm. Two regression analysis and three classification analysis models has been developed for rainfall prediction of 33 Bangladeshi weather station. Apache Spark library has been used for machine library in Scala programming language. The main idea behind the use of classification and regression analysis is to see the comparative difference between types of algorithms prediction output and the predictability along with usability. This thesis is a contribution to the effort of rainfall prediction within Bangladesh. It takes the strategy of applying machine learning models to historical weather data gathered in Bangladesh. As part of this work, a web-based software application was written using Apache Spark, Scala and HighCharts to demonstrate rainfall prediction using multiple machine learning models. Models are successively improved with the rainfall prediction accuracy. Content The given data has weather station and year wise monthly rainfall data of Bangladesh. Data is two format - 46 year (33 Weather Station) : From 1970 to 2016 Daily Rainfall Data Monthly Rainfall Data Columns: Station (Weather Station, along with Station Index) Year Month Day [For daily data file]
