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

• Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region [62.09891513612252]
  We focus on limited-area modeling and train our model specifically for localized region-level downstream tasks. We consider the MENA region due to its unique climatic challenges, where accurate localized weather forecasting is crucial for managing water resources, agriculture and mitigating the impacts of extreme weather events. Our study aims to validate the effectiveness of integrating parameter-efficient fine-tuning (PEFT) methodologies, specifically Low-Rank Adaptation (LoRA) and its variants, to enhance forecast accuracy, as well as training speed, computational resource utilization, and memory efficiency in weather and climate modeling for specific regions.
  arXiv  Detail & Related papers  (2024-09-11T19:31:56Z)
• Deep learning surrogate models of JULES-INFERNO for wildfire prediction on a global scale [10.16915256748265]
  Two data-driven models are built in this work based on Deep Learning techniques to surrogate the JULES-INFERNO model. More precisely, these machine learning models take global temperature, vegetation density, soil moisture and previous forecasts as inputs to predict the subsequent global area burnt. Results show a strong performance of the proposed models, in terms of both computational efficiency (less than 20 seconds for 30 years of prediction on a laptop CPU) and prediction accuracy (with AEP under 0.3% and SSIM over 98% compared to the outputs of JULES-INFERNO)
  arXiv  Detail & Related papers  (2024-08-30T20:05:00Z)
• 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: 3D absolute position 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 ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. 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)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• 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 Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  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)
• 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.