152 skills found · Page 1 of 6
mlco2 / ImpactML has an impact on the climate. But not all models are born equal. Compute your model's emissions with our calculator and add the results to your paper with our generated latex template
google / Travel Impact ModelEmission estimation model for flights
facebookresearch / Volumetric PrimitivesThis repository contains the implementation of our novel approach associated with the paper "Don't Splat Your Gaussians" to modeling and rendering scattering and emissive media using volumetric primitives with the Mitsuba renderer.
CMU-CREATE-Lab / Deep Smoke MachineDeep learning models and dataset for recognizing industrial smoke emissions
ClimateMARGO / ClimateMARGO.jlJulia implementation of MARGO, an idealized climate-economic modelling framework for Optimizing trade-offs between emissions Mitigation, Adaptation, carbon dioxide Removal, and solar Geoengineering.
smrt-model / SmrtSnow Microwave Radiative Transfer model to compute thermal emission, backscatter and altimetric waveform from snowpack, sea-ice, frozen lakes and other cryospheric environments
Akajiaku11 / Carbon Footprint Analysis And Emission ForecastingThis project is a Python-based tool for analyzing carbon emissions and forecasting future trends. It uses synthetic data to simulate historical emissions and applies ARIMA (a time series model) and Linear Regression (a machine learning model) to predict future emissions
CEMPD / SMOKECreate emissions inputs for multiple air quality modeling systems with unmatched speed and flexibility
atmoschem / VeinR+Fortran+OpenMP package to estimate Vehicular Emissions INventories VEIN.
atmoschem / EmissVCreate and processing emissions for numeric air quality models
DavieObi / Carbon Emissions Impact AnalysisDeep dive into global CO2 emissions & temperature changes. Quantifies trends, correlations, and explores lagged effects & causality. Utilizes clustering to identify climate patterns and builds a predictive model for "what-if" scenarios. Delivers critical insights into climate change impact for informed decision-making.
vrdmr / CS273a Introduction To Machine LearningIntroduction to machine learning and data mining How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike. This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques. Background We will assume basic familiarity with the concepts of probability and linear algebra. Some programming will be required; we will primarily use Matlab, but no prior experience with Matlab will be assumed. (Most or all code should be Octave compatible, so you may use Octave if you prefer.) Textbook and Reading There is no required textbook for the class. However, useful books on the subject for supplementary reading include Murphy's "Machine Learning: A Probabilistic Perspective", Duda, Hart & Stork, "Pattern Classification", and Hastie, Tibshirani, and Friedman, "The Elements of Statistical Learning".
nearform / Node Hidden Markov Model TfA trainable Hidden Markov Model with Gaussian emissions using TensorFlow.js
atmoschem / EixportExport Emissions to Atmospheric Models
Beckybams / AI For Livestock Emission TrackingAI for Livestock Emission Tracking uses artificial intelligence to estimate and analyze greenhouse gas emissions from livestock. By leveraging synthetic data and machine learning models, the system supports sustainable agriculture, improves environmental monitoring, and helps farmers.
Geraldine-Winston / Carbon Emission Forecasting Using LSTM Models.Harnessing LSTM neural networks, this project forecasts carbon emissions from historical data. Through streamlined preprocessing, dynamic modeling, and vivid visualizations, it transforms raw data into actionable, exportable insights for impactful environmental analysis and decision-making.
multiwavelength / GleamGalaxy Line Emission & Absorption Modeling
Beckybams / Greenhouse Gas Emission PredictionGreenhouse Gas Emission Prediction uses machine learning models to estimate future emissions based on historical environmental, industrial, and energy data. By analyzing patterns and trends, the system helps governments, researchers, and organizations forecast carbon output.
vikas9087 / Bilevel Optimization EmissionsProposed a mathematical model for optimizing the profits and emissions while setting dynamic prices of electricity. A bilevel & multi-objective model is proposed for maximizing profits of retailer, minimizing the emissions produced, & minimizing the total cost of customers.
CRossSchmidtlein / DPETSTEPDynamic PET simulator of tracers via emission projection for kinetic modeling and parametic image studies
