A comparative study of statistical and machine learning models on
near-real-time daily emissions prediction
- URL: http://arxiv.org/abs/2302.01152v1
- Date: Thu, 2 Feb 2023 15:14:27 GMT
- Title: A comparative study of statistical and machine learning models on
near-real-time daily emissions prediction
- Authors: Xiangqian Li
- Abstract summary: The rapid ascent in carbon dioxide emissions is a major cause of global warming and climate change.
This paper aims to select a suitable model to predict the near-real-time daily emissions from January 1st, 2020 to September 30st, 2022 of all sectors in China.
- Score: 0.0
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: The rapid ascent in carbon dioxide emissions is a major cause of global
warming and climate change, which pose a huge threat to human survival and
impose far-reaching influence on the global ecosystem. Therefore, it is very
necessary to effectively control carbon dioxide emissions by accurately
predicting and analyzing the change trend timely, so as to provide a reference
for carbon dioxide emissions mitigation measures. This paper is aiming to
select a suitable model to predict the near-real-time daily emissions based on
univariate daily time-series data from January 1st, 2020 to September 30st,
2022 of all sectors (Power, Industry, Ground Transport, Residential, Domestic
Aviation, International Aviation) in China. We proposed six prediction models,
which including three statistical models: Grey prediction (GM(1,1)),
autoregressive integrated moving average (ARIMA) and seasonal autoregressive
integrated moving average with exogenous factors (SARIMAX); three machine
learning models: artificial neural network (ANN), random forest (RF) and long
short term memory (LSTM). To evaluate the performance of these models, five
criteria: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean
Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of
Determination () are imported and discussed in detail. In the results, three
machine learning models perform better than that three statistical models, in
which LSTM model performs the best on five criteria values for daily emissions
prediction with the 3.5179e-04 MSE value, 0.0187 RMSE value, 0.0140 MAE value,
14.8291% MAPE value and 0.9844 value.
Related papers
- Optimizing PM2.5 Forecasting Accuracy with Hybrid Meta-Heuristic and Machine Learning Models [0.0]
This study focuses on forecasting hourly PM2.5 concentrations using Support Vector Regression (SVR)
Meta-heuristic algorithms, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) are used to enhance prediction accuracy.
Results show significant improvements with PSO-SVR (R2: 0.9401, RMSE: 0.2390, MAE: 0.1368) and GWO-SVR (R2: 0.9408, RMSE: 0.2376, MAE: 0.1373)
arXiv Detail & Related papers (2024-07-01T05:24:19Z) - EWMoE: An effective model for global weather forecasting with mixture-of-experts [6.695845790670147]
We propose EWMoE, an effective model for accurate global weather forecasting, which requires significantly less training data and computational resources.
Our model incorporates three key components to enhance prediction accuracy: meteorology-specific embedding, a core Mixture-of-Experts layer, and two specific loss functions.
arXiv Detail & Related papers (2024-05-09T16:42:13Z) - ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast.
We also introduce a training-free extreme value enhancement strategy named ExEnsemble, which increases the variance of pixel values and improves the forecast robustness.
Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
arXiv Detail & Related papers (2024-02-02T10:34:13Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Dealing with zero-inflated data: achieving SOTA with a two-fold machine
learning approach [0.18846515534317262]
This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results.
It is estimated that the proposed approach is also four times more energy efficient than the SOTA approach against which it was compared.
arXiv Detail & Related papers (2023-10-12T07:26:41Z) - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan.
textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes.
Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - A Comparative Study of Machine Learning Algorithms for Anomaly Detection
in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
This study seeks to address the demands of high-performance machine learning models with environmental sustainability.
Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance.
However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
arXiv Detail & Related papers (2023-07-01T15:18:00Z) - DiffESM: Conditional Emulation of Earth System Models with Diffusion
Models [2.1989764549743476]
A key application of Earth System Models (ESMs) is studying extreme weather events, such as heat waves or dry spells.
We show that diffusion models can effectively emulate the trends of ESMs under previously unseen climate scenarios.
arXiv Detail & Related papers (2023-04-23T17:12:33Z) - Clinical Deterioration Prediction in Brazilian Hospitals Based on
Artificial Neural Networks and Tree Decision Models [56.93322937189087]
An extremely boosted neural network (XBNet) is used to predict clinical deterioration (CD)
The XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data.
arXiv Detail & Related papers (2022-12-17T23:29:14Z) - DeepVol: Volatility Forecasting from High-Frequency Data with Dilated
Causal Convolutions [78.6363825307044]
We propose DeepVol, a model based on Dilated Causal Convolutions to forecast day-ahead volatility by using high-frequency data.
We show that the dilated convolutional filters are ideally suited to extract relevant information from intraday financial data.
arXiv Detail & Related papers (2022-09-23T16:13:47Z) - Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives
for Brazil [3.0711362702464675]
The new Coronavirus (COVID-19) is an emerging disease responsible for infecting millions of people since the first notification until nowadays.
In this paper, autoregressive integrated moving average (ARIMA), cubist (CUBIST), random forest (RF), ridge regression (RIDGE), and stacking-ensemble learning are evaluated.
The developed models can generate accurate forecasting, achieving errors in a range of 0.87% - 3.51%, 1.02% - 5.63%, and 0.95% - 6.90% in one, three, and six-days-ahead, respectively.
arXiv Detail & Related papers (2020-07-21T17:58:58Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.