Multi-Country GHG Emissions Forecasting by Sector Using a GCN-LSTM Model

Objectives: To compare time series prediction-based deep learning techniques with Numerical Weather Prediction (NWP) to identify the optimal model for weather forecasting based on accuracy and consistency. Methods: The study utilizes a weather dataset collected from Kaggle, containing meteorological parameters such as temperature, humidity, and wind speed. Deep learning models such as the Radial Basis Function Network, the Convolutional Neural Network, the Recurrent Neural Network, and the Long Short-Term Memory have been developed and trained using the dataset for time-series forecasting. The models have been trained and evaluated using standard performance metrics, MAE, MSE, RMSE, and R2. Their predictions have been compared with a traditional Numerical Weather Prediction model. Findings: An evaluation of NWP and the LSTM model demonstrates that the LSTM provides significantly better accuracy in weather predictions. The mean absolute error (MAE) for NWP model is 1.198, while the LSTM achieves a lower MAE of 0.734, indicating that the forecasts from the LSTM model are closer to the actual values on average. Correspondingly, the Mean Squared Error (MSE) decreases from 2.255 in the NWP model to 1.237 in the LSTM, and the Root Mean Squared Error (RMSE) reduces from 1.502 to 1.112, indicating a decrease in prediction errors. Most notably, the R2 (coefficient of determination) improves from 0.975 in the NWP model to 0.985 in the LSTM, showing that the LSTM model accounts for 98.5% of the variability in the weather data, compared to 97.5% with the NWP model. These results illustrate that the LSTM model surpasses the traditional NWP approach in terms of accuracy and reliability for weather forecasting. Novelty: The research offers a comparative evaluation of deep learning models, the CNN, RNN, LSTM, and RBFN, for weather forecasting using both numerical climate data and satellite images, offering an inclusive approach rarely addressed in existing studies. Keywords: CNN, RNN, LSTM, RBFN, NWP, Weather Forecasting

This study introduces a hybrid AutoRegressive Integrated Moving Average (ARIMA)—Long Short-Term Memory (LSTM) model for predicting and managing sugarcane pests and diseases, leveraging big data for enhanced accuracy. The ARIMA component efficiently captures linear patterns in time-series data, while the LSTM model identifies complex nonlinear dependencies. By integrating these two approaches, the hybrid model effectively handles both linear trends and nonlinear fluctuations, improving predictive performance over conventional models. The model was trained on 33 years of meteorological and pest occurrence data, and its effectiveness was evaluated using mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The results show that the ARIMA-LSTM model achieves an MSE of 2.66, RMSE of 1.63, and MAE of 1.34, outperforming both the standalone ARIMA model (MSE = 4.97, RMSE = 2.29, MAE = 1.79) and LSTM model (MSE = 3.77, RMSE = 1.86, MAE = 1.45). This superior performance highlights its ability to effectively capture seasonal variations and complex nonlinear patterns in pest outbreaks. Beyond accurate forecasting, this model provides valuable decision-making support for agricultural management, aiding in early intervention strategies. Future enhancements, including the integration of additional variables and climate change factors, could further expand its applicability across diverse agricultural sectors, improving crop yield stability and pest control strategies in an increasingly unpredictable climate.

Globally, wind energy if properly harnessed, could serve as a source of energy generation in Africa. This study compared the performance of two Machine Learning (ML) algorithms (Linear regression and Random Forest) in predicting wind speed in five major cities in Africa (Yaoundé, Pretoria, Nairobi, Cairo and Abuja). Wind data were collected between January 1, 2000, and December 31, 2022, using the Solar Radiation Data Archive. The data preprocessing was carried out with 80% of the data used for training and 20% for validation. The performance of these ML algorithms was evaluated using Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The result shows that Nairobi (3.814795 m/s) closely followed by Cairo (3.606453 m/s) has the highest mean wind speed while Yaoundé (1.090512 m/s) has the lowest. Based on the performance metrics used, the two Machine Learning algorithms were competitive. Still, the Linear Regression (LR) algorithm outperformed the Random Forest Algorithm in predicting wind speed in all the selected major African cities. In Yaoundé (RMSE = 0.3892, MAE= 0.3001, MAPE =0.5030), Pretoria (RMSE=1.2339, MAE=0.9480, MAPE=0.7450) Nairobi (RMSE= 0.4223, MAE =0.6499, MAPE =0.1872), Nairobi (RMSE=0.6499, MAE=0.5171, MAPE =0.1872), Cairo (RMSE =1.0909, MAE =0.8544, MAPE =0.3541) and Abuja (RMSE = 0.70245, MAE =0.5441, MAPE= 0.4515) the Linear regression algorithms was found to outperformed Random Forest Regression. Therefore, the Linear regression algorithm is more reliable in predicting wind speed compared with the Random Forest regression.

Introduction: Air pollution is a major environmental challenge worldwide and predicting air quality is key to regulating air pollution. The extent of air pollution is quantified by the Air Quality Index (AQI). Air quality forecasting has become an important area of research. Deep Neural Networks (DNN) are useful in predicting the AQI instead of traditional methods which involve numerous computations. The aim of this research paper is to investigate the use of the deep neural networks as a framework for predicting the air quality index based on time series data of pollutants.
 Materials and methods: To resolve this problem, the study proposes a DNN to develop the best model for predicting the AQI. Long Short-Term Memory (LSTM) and Bi-directional LSTM have been introduced in the study to understand and predict the relationship between the pollutants affecting the AQI. The model’s performance is evaluated using the metrics, Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and coefficient of determination (R2). To conduct the study, real-time hourly data for the period November 2017 to January 2020 from an air quality monitoring station was considered for the proposed capital region of the state of Andhra Pradesh in India.
 Results: The multivariate modeling considers seven pollutants as independent variables and AQI as the target variable. After experimenting and training the algorithm on the dataset, Bi-directional LSTM was shown to have the lowest MAE and RMSE values and the highest R2, indicating that it has the highest accuracy in AQI prediction.
 Conclusion: The development of a capital city involves massive construction activity resulting in air pollution. The results are helpful to the authorities to monitor the quality of air of develop air quality management programs thus avoiding the impact of air pollution on health.

Introduction: Forecasting electrical energy demand is crucial for predicting future energy consumption patterns, which aids in effective energy management and distribution. Various forecasting methods have been developed, yet this study explores univariate time series analysis using Bidirectional Long Short-Term Memory (BiLSTM) and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model. These deep learning techniques are designed to capture both temporal dependencies and spatial patterns, improving predictive performance in energy forecasting. Objectives: This study aims to evaluate the forecasting performance of deep learning models in univariate time series energy demand prediction. Specifically, it seeks to: Implement and compare the forecasting performance of Bidirectional LSTM and hybrid CNN-LSTM models using a publicly available dataset from Transmission Service Operators (TSO). Preprocess the dataset using appropriate data preparation techniques, such as normalization, handling missing values, and feature selection, before training the models. Assess predictive accuracy by evaluating both models using key performance metrics, including Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-Squared (R²). Methods: The dataset used in this study was obtained from a public portal for Transmission Service Operators (TSO). Before training, the data underwent preprocessing techniques such as normalization, handling missing values, and feature selection to improve model performance. Two deep learning models—BiLSTM and CNN-LSTM—were implemented and trained on the dataset. The performance of each model was evaluated using four key metrics: Mean Absolute Error (MAE) – measures the average magnitude of errors, Mean Absolute Percentage Error (MAPE) – represents error as a percentage of actual values, Root Mean Squared Error (RMSE) – penalizes larger errors more heavily than MAE, R-Squared (R²) – indicates how well predictions align with actual data. Results: Experimental findings reveal that the hybrid CNN-LSTM model outperformed the BiLSTM model across all evaluation metrics. The CNN-LSTM model achieved a lower MAE of 499.08 compared to 780.56 in BiLSTM, a lower MAPE of 1.80% versus 2.52%, and a reduced RMSE of 671.37 compared to 1,042.20. Additionally, the CNN-LSTM model obtained a slightly higher R² score of 0.97 compared to 0.94 in BiLSTM, indicating a better fit for the data. Conclusion: The results demonstrate that integrating CNN with LSTM significantly improves predictive accuracy in univariate time series energy demand forecasting. The CNN component enhances feature extraction, allowing the LSTM layers to capture complex temporal dependencies more effectively. Consequently, the hybrid CNN-LSTM model emerges as a more robust approach compared to BiLSTM alone, making it a valuable tool for accurate energy demand forecasting. Further research can explore additional deep learning architectures or hybrid models to optimize forecasting performance further.

Pandemics present critical challenges to global health systems, economies, and societal structures, necessitating the development of accurate forecasting models for effective intervention and resource allocation. Classical statistical models such as the autoregressive integrated moving average (ARIMA) have been widely employed in epidemiological forecasting; however, they struggle to capture the nonlinear trends and dynamic fluctuations inherent in pandemic data. Conversely, deep learning models such as long short-term memory (LSTM) networks demonstrate strong capabilities in modeling complex dependencies but often require substantial data and computational resources. To boost forecasting precision, hybrid models such as ARIMA-LSTM integrate the advantages of traditional and deep learning methods. This study evaluates and compares the performance of ARIMA, LSTM, and hybrid ARIMA-LSTM models in predicting pandemic trends, using COVID-19 data from the Malaysian Ministry of Health as a case study. The dataset covers the period from 4 January 2021 to 18 September 2021, and model performance is evaluated using key metrics, including mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), root mean squared error (RMSE), relative root mean squared error (RRMSE), normalized root mean squared error (NRMSE), and the coefficient of determination (R2). The results demonstrate that ARIMA performs poorly in capturing pandemic trends, while LSTM improves forecasting accuracy. However, the hybrid ARIMA-LSTM model consistently achieves the lowest error rates, confirming the advantage of integrating statistical and deep learning methodologies. All findings support the adoption of hybrid modeling approaches for pandemic forecasting, contributing to more accurate and reliable predictive analytics in epidemiology. Future research should investigate the generalizability of hybrid models across various infectious diseases and integrate additional real-time external variables to improve forecasting reliability.

Accurate prediction of greenhouse temperatures is essential for developing effective environmental control strategies, as the precision of minimum temperature data acquisition significantly impacts the reliability of predictive models. Traditional monitoring methods face inherent challenges due to the conflicting demands of temperature-field uniformity assumptions and the costs associated with sensor deployment. This study introduces an ARIMA-Kriging spatiotemporal coupling model, which combines temperature time-series data with sensor spatial coordinates to accurately determine minimum temperatures in greenhouses while reducing hardware costs. Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. Across different prediction horizons (10 min and 30 min intervals), the GWO-BiLSTM model demonstrated superior performance with key metrics reaching a coefficient of determination (R2) of 0.97, root mean square error (RMSE) of 0.79–0.89 °C (41.7% reduction compared to the PSO-BP model), mean absolute percentage error (MAPE) of 4.94–8.5%, mean squared error (MSE) of 0.63–0.68 °C, and mean absolute error (MAE) of 0.62–0.65 °C, significantly outperforming the BiLSTM, LSTM, and PSO-BP models. Multi-weather validation confirmed the model’s robustness under rainy, snowy, and overcast conditions, maintaining R2 ≥ 0.95. Optimal prediction accuracy was observed in clear weather (RMSE = 0.71 °C), whereas rainy/snowy conditions showed a 42.9% improvement in MAPE compared to the PSO-BP model. This study provides reliable decision-making support for precise environmental regulation in facility greenhouse environments, effectively advancing the intelligent development of agricultural environmental control systems.

Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators.

Forecasting daily PM2.5 concentrations in Wuhan with a spatial-autocorrelation-based long short-term memory model

This article uses machine learning techniques as fuzzy and neuro-fuzzy ANFISs, to develop and compare prediction models capable of relating pregnant women’s exposure to air pollutants, such as Nitrogen Dioxide and Particulate Matter, the mother’s age, and the number of prenatal consultations to the incidence of premature birth. In the current literature, studies can be found that relate prematurity to the exposure of pregnant women to NO2, O3, and PM10; to Toluene and benzene, mainly in the window 5 to 10 days before birth; and to PM10 in the week before birth. Both models used logistic regression to quantify the effects of pollutants as a result of premature birth. Datasets from Brazil—Departamento de Informatica do Sistema Único de Saúde (DATASUS) and Companhia Ambiental do Estado de São Paulo (CETESB)—were used, covering the period from 2016 to 2018 and comprising women living in the city of São José dos Campos (SP), Brazil. In order to evaluate and compare the different techniques used, evaluation metrics were calculated, such as correlation (r), coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Mean Absolute Error (MAE). These metrics are widely used in the literature due to their ability to evaluate the robustness and efficiency of prediction models. For the RMSE, MAPE, MSE, and MAE metrics, lower values indicate that prediction errors are smaller, demonstrating better model accuracy and confidence. In the case of (r) and R2, a positive and strong result indicates alignment and better performance between the real and predicted data. The neuro-fuzzy ANFIS model showed superior performance, with a correlation (r) of 0.59, R2 = 0.35, RMSE = 2.83, MAPE = 5.35%, MSE = 8.00, and MAE = 1.70, while the fuzzy model returned results of r = 0.20, R2 = 0.04, RMSE = 3.29, MSE = 10.81, MAPE = 6.67%, and MAE = 2.01. Therefore, the results from the ANFIS neuro-fuzzy system indicate greater prediction capacity and precision in relation to the fuzzy system. This superiority can be explained by integration with neural networks, allowing data learning and, consequently, more efficient modeling. In addition, the findings obtained in this study have potential for the formulation of public health policies aimed at reducing the number of premature births and promoting improvements in maternal and neonatal health.

Objectives: Predicting the amount of rainfall is difficult due to its complexity and non-linearity. The objective of this study is to predict the average rainfall one month ahead using the all-India monthly average rainfall dataset from 1871 to 2016. Methods: This study proposed a Bidirectional Long Short-Term Memory (LSTM) model to predict the average monthly rainfall in India. The parameters of the models are determined using the grid search method. This study utilized the average monthly rainfall as an input, and the dataset consists of 1752 months of rainfall data prepared from thirty (30) meteorological sub-divisions in India. The model was compiled using the Mean Square Error (MSE) loss function and Adam optimizer. The models' performances were evaluated using statistical metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Findings: This study discovered that the proposed Bidirectional LSTM model achieved an RMSE of 240.79 and outperformed an existing Recurrent Neural Network (RNN), Vanilla LSTM and Stacked LSTM by 8%, 4% and 2% respectively. The study also finds that increasing the input time step and increasing the number of cells in the hidden layer enhanced the prediction performance of the proposed model, and the Bidirectional LSTM converges at a lower epoch compared to RNN and LSTM models. Novelty: This study applied the Bidirectional LSTM for the first time in predicting all-India monthly average rainfall and provides a new benchmark for this dataset. Keywords: Deep Learning, LSTM, Rainfall prediction, Stacked LSTM, Bidirectional LSTM

The core problem for decision-makers lies in selecting an effective forecasting method, particularly when faced with the challenges of nonlinearity and nonstationarity in time series data. To address this, hybrid models are increasingly employed to enhance forecasting accuracy. In Indonesia and other Muslim countries, monthly economic and business time series data often include trends, seasonality, and calendar variations. This study compares the performance of the hybrid Prophet-Long Short-Term Memory (LSTM) model with their individual counterparts to forecast such patterned time series. The aim is to identify the best model through a hybrid approach for forecasting time series data exhibitingtrend, seasonality, and calendar variations, using the real-life case of currency circulation in South Sulawesi. The goodness of the models is evaluated using the smallest Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) values. The results indicate that the hybrid Prophet- LSTM model demonstrates superior accuracy, especially for predicting currency outflow, with lower MAPE and RMSE values than standalone models. The LSTM model shows excellent performance for currency inflow, while the Prophet model lags in inflow and outflow accuracy. This insight is valuable for Bank Indonesia’s strategic planning, aiding in better cash flow prediction and currency stock management.

During milling operations, wear of cutting tool is inevitable; therefore, tool condition monitoring is essential. One of the difficulties in detecting the state of milling tools is that they are visually inspected, and due to this, the milling process needs to be interrupted. Intelligent monitoring systems based on accelerometers and algorithms have been developed as a part of Industry 4.0 to monitor the tool wear during milling process. In this paper, acoustic emission (AE) and vibration signals captured through sensors are analyzed and the scalograms were constructed from Morlet wavelets. The relative wavelet energy (RWE) criterion was applied to select suitable wavelet functions. Due to the availability of less experimental data to train the LSTM model for the prediction of tool wear, SinGAN was applied to generate additional scalograms and later several image quality parameters were extracted to construct feature vectors. The feature vector is used to train three long short-term memory network (LSTM) models: vanilla, stacked, and bidirectional. To analyze the performance of LSTM models for tool wear prediction, five performance parameters were computed namely R2, adjusted R2, mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE). The lowest MAE, RMSE, and MSE values were observed as 0.005, 0.016, and 0.0002 and high R2 and Adj. R2 values as 0.997 are observed from the vibration signal. Results suggest that the stacked LSTM model predicts the tool wear better as compared to other LSTM models. The proposed methodology has given very less errors in tool wear predictions and can be extremely useful for the development of an online deep learning tool condition monitoring system.

The growing societal concern regarding environmental matters has led to the implementation of many environmental measures intended to protect the environment and address global warming by lessening emissions and mitigating climate change. In line with this movement, this study scrutinizes the impact of these environmental measures on greenhouse gas (GHG) emissions to analyze the cases of Finland and Sweden. More specifically, the study employs the Environmental Policy Stringency (EPS) index as a proxy for environmental measures, explores sector-specific GHG emissions by employing nonlinear quantile-based methodologies (including quantile-on-quantile regression and Granger causality-in-quantiles methods as the primary model and quantile regression for robustness checking) spanning the period from 1991/Q1 to 2020/Q4. The findings show that: (i) EPS lessens GHG emissions from fuel exploitation, industrial combustion, and the power industry sector at lower and middle quantiles in Finland and Sweden; (ii) EPS decreases GHG emissions from processes, transportation, and waste sectors in Finland but increases them in Sweden at higher quantiles; (iii) EPS leads to an increase in GHG emissions from the agriculture and construction sectors at higher quantiles; (iv) EPS has a causal effect on sector-specific GHG emissions across different quantiles; (v) the robustness of the findings is largely confirmed. Hence, the study underscores the varying impacts of EPS on sectoral GHG emissions based on quantiles, sectors, and countries, emphasizing the need for policymakers to adopt environmental policies to comprise these differences and adjust the policy framework accordingly.

Cryptocurrencies have emerged as one of the most popular digital assets, characterized by high volatility, which presents a significant challenge in forecasting their price movements accurately. This study aims to implement the Long Short-Term Memory (LSTM) algorithm to predict the prices of selected cryptocurrencies, including Bitcoin (BTC), Binance Coin (BNB), Ethereum (ETH), Dogecoin (DOGE), Solana (SOL), and Shiba Inu (SHIB). The LSTM model is trained using the Adam optimizer and employs early stopping to mitigate overfitting. Model performance is evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The results indicate that the LSTM model achieves strong predictive accuracy for relatively low-volatility assets such as Dogecoin and Solana, with R² scores of 0.9795 and 0.9523, respectively. In contrast, its performance declines when applied to highly volatile assets like Bitcoin and Binance Coin. The findings also suggest that LSTM performs best in short-to-medium-term forecasts (7 to 30 days), but shows limitations in long-term predictions. This study contributes to the field by demonstrating the applicability of LSTM in financial forecasting and highlighting its strengths and constraints across different volatility profiles. Practically, the findings can assist traders and financial analysts in making data-driven decisions by applying LSTM models for more reliable short-term predictions, while emphasizing the need to integrate external market factors to enhance long-term forecast accuracy.