GayeBalkanli's Stars
cerea-daml/fnn
Toolbox to use NNs in fortran
DSIP-FBK/GPTCast
GPTCast model for precipitation nowcasting
6x16/HK_Central-roadside-PM-monitoring_Machine-Learning
Machine learning modelling for HK roadside PM concentration prediction
ML-precip/precip-predict
Precipitation prediction with Machine Learning
katiedagon/ML-extremes
Repository for project using machine learning (ML) for precipitation extremes
atifferoz/Snowfall-Prediction-using-Machine-Learning
In this research, Machine learning algorithms like Long-Short Term Model (LSTM), Decision tree, Random Forest and XG Boost were used as a classifier to improve the accuracy of Snowfall prediction for the region of Boston. The geographical parameters like Humidity, Temperature, Wind-speed, Precipitation, Sea-level, Dew-point and Visibility were used as independent variables. Before the modeling phase, Data lagging was performed for 2 step followed by Exploratory Data Analysis was using techniques like Multiple Linear Regression, Correlation Plot and variable importance plot. Feature Selection was also executed using Logistic Regression and Boruta algorithm. Experimental evaluations resulted in the highest accuracy shown by LSTM with an accuracy of 89.98%. In terms of sensitivity, Random Forest outperformed other classifier models. Whereas, Decision tree and XG Boost resulted well in the overall performance of prediction with respect to other evaluation metrics. The results of this research added to the contribution of the knowledge in weather prediction in the domain of Snowfall for the machine learning industry.
neetika6/Machine-Learning-Model-for-Weather-Forecasting
Purpose of this project is to predict the temperature using different algorithms like linear regression, random forest regression, and Decision tree regression. The output value should be numerically based on multiple extra factors like maximum temperature, minimum temperature, cloud cover, humidity, and sun hours in a day, precipitation, pressure and wind speed.
prajakt1206/weather_prediction
This is the weather prediction machine learning model which ll help to predict different parameter of weather like, Temperature, wind speed, air pollution index.
SimeonDee/pm2_5_air_pollution_prediction_and_forecast
Analysis of Air Pollution prediction and time-series forecast of PM2.5 Pollutant using Machine Learning Algorithms (SVM, Decision Tree and Random Forest) and Deep Learning Algorithms (CNN, Bi-LSTM). Also considered for improved performances is random search hyper-parameter tuning using Ray-Tune with HyperBand Scheduler strategy.
earthlab/Western_states_daily_PM2.5
Earth Lab's health team project to estimate air pollution exposures across the western U.S. for an 11-year period (2008-2018). R code to execute the machine learning algorithms that will be used to estimate PM2.5.
joshmalina/linear_and_logistic_regression
Using machine learning techniques to predict air pollution.
Yajingleo/Machine-Learning-China-Air-Pollution
This hub is for a project for research on China air pollution PM 2.5, including research references, data sources, and a list of our codes and result.
pujeethaaj/Analysis-of-air-pollution-levels-and-its-contributing-factors-using-Machine-Learning-models
Used various machine learning models to predict the particulate matter(pm2.5) value for the pollution data. Discovered an interesting pattern in the outlier data and analyzed it further to understand the data's revelation. Achieved an RMSE score of around 20 - 21
ParaicOReilly/Final-Year-Project-4th-Year
INVESTIGATING THE PERFORMANCE OF DIFFERENT MACHINE LEARNING MODELS PREDICTING PARTICULATE MATTER IN DUBLIN
vimalraj-76/An-algorithmic-approach-for-pollution-monitoring-and-predicting
Over the past few decades, due to human activities, industrialization, and urbanization, air pollution has become a life-threatening factor in many countries around the world. Air pollution has a direct impact on human health through the exposure of pollutants and particulates, which has increased the interest in air pollution and its impacts among the scientific community. The main causes associated with air pollution are the burning of fossil fuels, agriculture, exhaust from factories and industries, residential heating, and natural disasters. The Environmental Protection Agency (EPA) tracks the pollution level by calculating the amount of ground-level ozone (O3), Sulphur dioxide (SO2), particulates matter (PM10 and PM2.5), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) present in the air molecule. These substances are in compositions of a common index, called the Air Quality Index (AQI), indicating how clean or polluted the air is currently or forecasted to become in areas. This work deliberates the implementation of cloud based IoT system for air quality monitoring in which the sensors are used to calculate CO, PM2.5 and PM10, O3, SO2 and NOx pollution level with environmental condition like temperature and humidity. The obtained information can be updated in cloud platform using Lora nodes and Lora Gateway. The information fetched from the cloud is transmitted to the Machine learning models which contains the detailed dataset for the pollutants and these models accurately predict the day-wise pollutant concentrations and display them using an application. This work presents the detailed analysis for predicting the cause of pollution by using Support Vector Machine (SVM), Random forest algorithm and K-nearest neighbors (KNN) algorithm.
sadikturan/python-dersleri
Python Programlama, Masaüstü Uygulamaları, Web Geliştirme ve Daha Fazlası