• Corpus ID: 12439970

Machine Learning Applied to Weather Forecasting

@inproceedings{Holmstrom2016MachineLA,
  title={Machine Learning Applied to Weather Forecasting},
  author={Mark A. Holmstrom and Dylan Liu},
  year={2016}
}
Weather forecasting has traditionally been done by physical models of the atmosphere, which are unstable to perturbations, and thus are inaccurate for large periods of time. Since machine learning techniques are more robust to perturbations, in this paper we explore their application to weather forecasting to potentially generate more accurate weather forecasts for large periods of time. The scope of this paper was restricted to forecasting the maximum temperature and the minimum temperature… 

Figures and Tables from this paper

Smart Weather Forecasting Using Machine Learning: A Case Study in Tennessee

This paper presents a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models, which can provide usable forecasts about certain weather conditions for the near future within a very short period of time.

Rainfall Prediction using Multiple Linear Regressions Model

A multiple linear regression model is developed in order to predict the rate of precipitation (PRCP), i.e., rainfall rate, for Khartoum state based on some weather parameters, such as temperature, wind speed, and dew point.

Nowcasting Precipitation Using Weather Radar Data for Lithuania : the First Results

First results of the nowcasting algorithms are presented, which are to be improved by further research, to predict a short-term precipitation over Lithuania using weather radar images provided by Lithuanian Hydrometeorology service.

An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature,

A Deep Learning-Based Weather Forecast System for Data Volume and Recency Analysis

A deep learning-based weather forecast system is proposed and data volume and recency analysis is conducted by utilizing a real-world weather data set as a case study to demonstrate the learning ability of deep learning model.

A Neuro Model for Weather Forecasting

  • T.G. PredunăV.A. RusuD. Năstac
  • Computer Science, Environmental Science
    2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME)
  • 2020
The capabilities, advantages and shortcomings of neural networks in the context of weather prediction are presented in order to show that weather forecasting can be done in a cost-efficient manner.

Weather Prediction Using Machine Learning Algorithms

This paper is predicting the weather by analyzing features like temperature, apparent temperature, humidity, wind speed, wind bearing, visibility, cloud cover with Random Forest, Decision Tree, MLP classifier, Linear regression, and Gaussian naive Bayes are examples of machine learning methods.

Interpretable Machine Learning for Meteorological Data

A new approach using interpretable machine learning for explaining the characteristic variables of meteorological data using machine learning models for the extraction of knowledge in the data is provided.

A Multi-class Classification Approach for Weather Forecasting with Machine Learning Techniques

Weather forecasting is vital as extreme weather events can cause damage and even death. The science of meteorology in recent decades has made spectacular progress resulting in more reliable

A comparative study of prediction and classification models on NCDC weather data

A set of the most common machine learning techniques explored to generate robust weather forecasting model for long periods of time are explored and experimental results of the classifiers show that the decision tree CART, XGBoost and AdaBoost models exhibit better classification accuracy when compared with the other methods.

References

SHOWING 1-9 OF 9 REFERENCES

Intelligent weather forecast

  • L. LaiH. Braun L. Yang
  • Environmental Science
    Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826)
  • 2004
A methodology to short-term temperature and rainfall forecasting over the east coast of China based on some necessary data preprocessing technique and the dynamic weighted time-delay neural networks (DWTDNN), in which each neuron in the input layer is scaled by a weighting function that captures the temporal dynamics of the biological task.

The accuracy of weather forecasts for Melbourne, Australia

An analysis of the accuracy, and trends in the accuracy, of medium‐range weather forecasts for Melbourne, Australia, is presented. The analysis shows that skill is evident in forecasts of

Atmospheric Temperature Prediction using Support Vector Machines

Non linear regression method is found to be suitable to train the SVM for weather prediction and the results are compared with Multi Layer Perceptron (MLP) trained with back-propagation algorithm and the performance of SVM is finding to be consistently better.

Bayesian Networks for Probabilistic Weather Prediction

This work introduces Bayesian Networks (BNs) in this framework to model the spatial and temporal dependencies among the different stations using a directed acyclic graph and shows how standard analog techniques are a special case of the proposed methodology when no spatial dependencies are considered in the model.

CS229 Lecture Notes Supervised Learning

  • 2016

Weather Underground, The Weather Company, 2016

  • [Online]. Available: https://www.wunderground.com/us/ca/paloalto/zmw:94305.1.99999. Accessed: Nov
  • 2016

” Intelligent weather forecast . ” Machine Learning and Cybernetics ,

  • ” CS 229 Lecture Notes Supervised Learning ”
  • 2004