Machine learning to better predict and understand drought. Moving github.com/ml-clim

Analyze NDVI trends and use LSTM to predict drought onset in arid regions.

Context 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]

📊🛰️ Data processing scripts, ML models, and Explainable AI results created as part of my Masters Thesis @ Johns Hopkins

Predicting droughts using ML.

Drought prediction and understanding with deep learning

Deep Learning Model to predict flash drought in an area.

Complete end-to-end MLOps implementation for training, maintaining and monitoring a machine learning model that predicts droughts.

Drought Prediction using Machine Learning - Developed a web based system to present the drought prone regions in USA. Aim is to reduce the loss of farmers due to inaccurate estimation of rainfall. Prediction of Statewise Drought index calculated using Palmer Index using a Artificial Neural network. Used Keras framework to implement a forward feeded network. Feb 2019 - April 2019.

This repository contains code, models, and resources that enable the development of predictive models to assess and forecast drought severity.

This repository comprises of the python code for drought prediction using AI/ML based models. The input used will be remote sensing based.

This repository contains the codes for my ICPRS'23 published paper titled "ML-based Vegetative Drought Prediction employing Satellite Remote Sensing and Precipitation Datasets".

🌍 AI-powered climate risk & disaster management system 🤖 ML predictions, interactive maps, satellite analysis ⚠️ Real-time alerts for floods, droughts, wildfires 🚀 Built with Python, Streamlit, TensorFlow, OpenCV

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Repo for testing various machine learning strategies for NOAA CPO Coping with Drought project

A tutorial for drought prediction using random forest. This has been created for the CLEX Winter School in Hobart, June 2023.

Using interpretable Machine Learning to decode the soil microbiome’s response to drought stress: Identifying marker taxa for drought stress using interpretable ML with SHAP values and conventional Differential Abundance Analysis tools.

This Project is to predict the drought area percentage in California state using Machine Learning and Deep Learning Algorithms

This repository contains the codes for my IAICT'23 published paper titled "Predictive Modeling of Vegetative Drought Using ML/DL Approach on Temporal Satellite Data".

Code to process data to train a machine learning DroughtFormer, built in the CREDIT framework, to predict drought related variables (ML model is trained globally, but there is a regional focus on Africa). Also contains code to analyze DroughtFormer predictions.

Detect forest disturbances (e.g., fire, drought) using satellite time-series data (1985–2018) at the pixel level. Each pixel has yearly spectral data, and the goal is to classify whether a disturbance occurred. Feature engineering and ML modeling are required; performance is measured by F1-score.

Misa is an AI/ML platform that makes ecological data accessible to everyone. It tracks animal migration patterns, predicts future routes, and links these shifts to climate change, deforestation, and droughts. With interactive maps, explainable predictions, and narrative stories, Misa transforms complex science into engaging insights for the public.

Final project for EAEEE4000

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一个初步的项目，尝试基于ENSO温度异常使用两种不同的机器学习模型来预测干旱程度。 同时，它也可以作为对机器学习工作原理以及不同模型性能的初步解释。

Project is Limited to look for Drought tagged Tweets streaming from obvious source. Based on NLTK, SCIPY, PANDAS, TK

architecture for Drought ML server

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Forecasting Drought Severity in U.S. Counties Using ML on Historical Weather Data (2011–2020)

Detecting drought impact on plants with hyperspectral images, spectral indices, and machine learning