192 skills found · Page 1 of 7
monocongo / Climate IndicesClimate indices for drought monitoring
Liquid-Prep / Liquid PrepLiquid Prep offers an end-to-end solution for farmers looking to optimize their water usage, especially during times of drought.
martinvonk / SPEIA Python package for calculating and visualizing drought indices such as the SPI, SPEI and SGI.
ECMWFCode4Earth / Ml DroughtMachine learning to better predict and understand drought. Moving github.com/ml-clim
brmagnuson / LandFallowingInEarthEngineThese scripts show how I used Google Earth Engine to estimate Central Valley land fallowed due to drought between 2010 and 2015.
rittmananalytics / DroughtyDroughty helps keep your workflow dry
wandb / DroughtwatchWeights & Biases benchmark for drought prediction
fsn1995 / Drought AnalysisDrought analysis with Google Earth Engine. (Compare SPEI with NDVI anomalies)
JohnNay / ForecastVegA Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python
royalosyin / Calculate Precipitation Based Agricultural Drought Indices With PythonPrecipitation-based indices are generally considered as the simplest indices because they are calculated solely based on long-term rainfall records that are often available. The mostly used precipitation-based indices consist of Decile Index (DI) Hutchinson Drought Severity Index (HDSI) Percen of Normal Index (PNI) Z-Score Index (ZSI) China-Z Index (CZI) Modified China-Z Index (MCZI) Rainfall Anomaly Index (RAI) Effective Drought Index (EDI) Standardized Precipitation Index (SPI).
Beckybams / Soil Moisture Prediction From Satellite DataSoil Moisture Prediction from Satellite Data uses satellite-derived features and machine learning to estimate soil moisture levels accurately. The project reduces dependence on ground sensors, supports precision agriculture, improves irrigation planning, and aids drought monitoring through scalable
ml-clim / Drought PredictionA Machine Learning Pipeline to Predict Vegetation Health
Dogiye12 / Drought Monitoring With NDVI MLAnalyze NDVI trends and use LSTM to predict drought onset in arid regions.
julherest / Drought ClustersCode used to identify and analyze drought clusters from gridded data.
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]
Beckybams / Wildfire Risk PredictionWildfire Risk Prediction is a machine learning project that uses synthetic environmental data to estimate wildfire probability. It analyzes factors like temperature, humidity, wind speed, rainfall, and drought index to classify risk levels.
Sibada / ScPDSICalculate the Self-Calibrating Palmer Drought Severity Index (scPDSI)
WillemMaetens / StandaRdizedR package for the calculation of Standardized Index values (SPI, SPEI, SSI,...) on a daily basis.
yousseftfifha / Groundwater Management Under Climate ChangeThis project aims to study the impact of climate change on groundwater level in Mornag plain in Tunisia. Indeed, in the last few decades, aquifers all over the world have experienced notable water level variability due to the spatiotemporal variability of rainfall and temperature. Therefore, for a reliable groundwater management under climate change context, it is mandatory to analyze and estimate its level variability. In this study, we focus on the plain of Mornag, located in the southeast of Tunisia, since it represents 33% of the national agricultural production. From this plain, we have collected historical piezometric and pluviometric data covering the period 2005-2017. Knowing the pluviometric data, our goal is to predict the piezometric one. This issue has been already studied using classical numerical groundwater modeling such as Modflow and Feflow. Despite unsatisfactory results, these techniques are data and time consuming. To overcome all these drawbacks, we propose to use two Artificial Intelligence (AI) approaches: the Extreme Gradient Boosting (XGBoost) approach, that has shown great performances in literature, and the well used one in our context which involves the use of Long-Short Term Memory (LSTM) Neural Network. For better results, we have added supplementary features to our dataset such as the cluster zone (zones with same characteristics) and the Standardized Precipitation Index (SPI) which can identify drought at different time scales. Both approaches have been executed entirely on GPU for time acceleration. Compared with traditional existing methods, they both have shown a high level of accuracy which confirms their adequacy for groundwater level forecasting. The proposed prediction models will be used for evaluating the repercussions of climate change on groundwater levels under the different scenarios RCP 4.5 and RCP 8.5 for the period of 2017-2090. It will be evaluated for three future periods: 2017-2040 (short term), 2041-2065 (medium term) and 2066-2090 (long term). The analysis of the future results using AI will be considered as a new Decision Support System used to optimize the management of our limited resources in order to satisfy the needs of the population in terms of drinking water and agriculture production.
adrHuerta / Drought Featuresrun theory to characterize drought indices
