Revolutionizing Air Pollution Spikes Analysis With a Blockchain‐Driven Machine Learning Framework

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms

In India, the sparse air quality monitoring infrastructure beyond urban centers limits our understanding of fine particulate matter (PM2.5) spatiotemporal variability, exposure dynamics, and their health and ecological impacts. To address this challenge, the Ambient Air Quality Monitoring in Rural Areas using Indigenous Technology (AMRIT) project was launched, deploying 1,400 low-cost air quality sensors (LCS) across diverse demographics and land-use settings in the states of Uttar Pradesh and Bihar, within the Indo-Gangetic plains (IGP). Additionally, the Dynamic Hyper-local Source Apportionment (DHSA) project was initiated to explore the potential of LCS for real-time source apportionment, facilitating pollution source identification and providing policymakers with critical data for effective air pollution control. Together, these initiatives aim to capture regional pollution dynamics and impacts beyond urban centers for the first time in India.Our investigation showed that the AMRIT-LCS network effectively captured fine-scale pollution patterns across urban and rural areas of Bihar, with a mean concentration of 135 µg/m³. Rural areas exhibited 6-8% higher pollution levels than urban settings. Within urban areas, pollution levels were notably higher in suburban and small-town regions, highlighting the need for targeted pollution management in these emerging cities. Leveraging machine learning (ML) frameworks, the village-level exposure assessment revealed significant spatiotemporal heterogeneity in PM2.5 exposure, with consistently high levels in the northwestern districts and a mortality rate (per lakh) of 50 (95% CI: 33–64) in the region. Data-driven techniques were applied to delineate air sheds for future air quality management. Five distinct air-sheds were identified, with three (in the north) and two (in the south) of the Ganges River, flowing west to east and dividing Bihar into two unequal parts. Consistent with the exposure model, the air-sheds over the northwestern and north-central regions were more polluted. Additionally, the LCS network was used to identify pollution hotspots by leveraging satellite-driven datasets within a deep learning framework, across various temporal scales.These findings underscore the critical need for high-density monitoring networks to capture local variability and generate spatially resolved estimates. Under the DHSA project, ML techniques enabled real-time, accurate predictions of pollution source contributions using LCS datasets, with a mean absolute error of less than 5%. This approach strengthens the potential for deploying widespread LCS networks capable of high-resolution source apportionment with spatiotemporal precision. The data-driven approach provides a solid foundation for designing decentralized mitigation strategies and comprehensive policy frameworks aimed at improving air quality and public health in the IGP. Thus, the portable LCS technology offers a promising alternative to conventional air quality monitoring methods, presenting a viable solution not only for India but also for data-sparse regions globally, contributing to the achievement of sustainable development goals.Keywords: PM2.5, Low-Cost Sensors, Indo-Gangetic Plains, Machine Learning, Exposure Assessment, Urban-Rural Disparity, Airsheds, Hotspot Analysis, Source Apportionment

Monkeypox virus is gaining attention due to its severity and spread among people. This study sheds light on the modeling and forecasting of new monkeypox cases. Knowledge about the future situation of the virus using a more accurate time series and stochastic models is required for future actions and plans to cope with the challenge. We conduct a side-by-side comparison of the machine learning approach with the traditional time series model. The multilayer perceptron model (MLP), a machine learning technique, and the Box-Jenkins methodology, also known as the ARIMA model, are used for classical modeling. Both methods are applied to the Monkeypox cumulative data set and compared using different model selection criteria such as root mean square error, mean square error, mean absolute error, and mean absolute percentage error. With a root mean square error of 150.78, the monkeypox series follows the ARIMA (7,1,7) model among the other potential models. Comparatively, we use the multilayer perceptron (MLP) model, which employs the sigmoid activation function and has a different number of hidden neurons in a single hidden layer. The root mean square error of the MLP model, which uses a single input and ten hidden neurons, is 54.40, significantly lower than that of the ARIMA model. The actual confirmed cases versus estimated or fitted plots also demonstrate that the multilayer perceptron model has a better fit for the monkeypox data than the ARIMA model. When it comes to predicting monkeypox, the machine learning method outperforms the traditional time series. A better match can be achieved in future studies by applying the extreme learning machine model (ELM), support vector machine (SVM), and some other methods with various activation functions. It is thus concluded that the selected data provide a real picture of the virus. If the situations remain the same, governments and other stockholders should ensure the follow-up of Standard Operating Procedures (SOPs) among the masses, as the trends will continue rising in the upcoming 10 days. However, governments should take some serious interventions to cope with the virus. In the ARIMA models selected for forecasting, we did not incorporate the effect of covariates such as the effect of net migration of monkeypox virus patients, government interventions, etc.

Household saving rate is an indispensable reference for people to grasp household economic behavior and analyze macroeconomic trends, and has a significant impact on capital accumulation, consumption and policy design. Taking UK household saving rate as the research object, this study compares the forecasting ability of Autoregressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model systematically based on the quarterly data from 2007Q1 to 2025Q1. Due to the economic fluctuations shown in UK from 2007Q1 to 2025Q1, it is particularly valuable to identify a robust model to forecast it. The empirical results show that the ETS (M, N, M) model is more robust than ARIMA (1,0,0) model in terms of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in out-of-sample forecasting. The in-sample fit of ARIMA model is better, but it may be over-fitted. This study can provide a reliable basis for the model selection of forecasting UK household saving rate, and the methodological approach and research conclusions of this study are also available for other similar countries.

The arising air pollution has addressed much attention globally due to its detrimental effects on human health and environment. As an early warning system for air quality control and management, it is important to provide precise information about the future concentrations in pollutants. We present here a time series model in predicting the Air Pollution Index (API) from three different stations; industrial, residential, and sub-urban areas between 2000 and 2009. In this paper, the Box–Jenkins approach of seasonal autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and three models of fuzzy time series (FTS) have been compared by using the mean absolute percentage error, mean absolute error, mean square error, and root mean square error. Although all the methods were used as operational tools, the ANN seemed more accurate in forecasting API. The results showed that FTS (i.e. Chen’s, Yu’s, and Cheng’s) performed inconsistent results since the conventional methods of ARIMA outperformed the performance of FTS. However, consistent results were achieved as the ANNs gave the smallest forecasting error compared to FTS and ARIMA.

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Machine learning based approach for modeling and forecasting of GPS–TEC during diverse solar phase periods

Air pollution, particularly fine particulate matter (PM2.5), poses a significant threat to public health in urban areas. In Almaty, Kazakhstan, high PM2.5 concentrations require effective forecasting methods to support timely intervention and policy planning. This study aims to evaluate and compare the performance of traditional statistical models and their hybrid counterparts for PM2.5 prediction. Multiple Linear Regression (MLR), Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Generalized Additive Models (GAM), and several hybrid combinations (e.g., MLR + GAM) were applied to daily air quality and meteorological data from February 2020 to May 2024. Missing values were imputed using Multiple Imputation by Chained Equations (MICE), and model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score. The results show that MLR provided the best explanatory power (R² = 0.7160), while SARIMA achieved the lowest RMSE (0.2719), indicating strong short-term predictive accuracy. Among hybrid models, MLR + GAM delivered the most promising results (R2 = 0.6124), although improvements over standalone models were limited. These findings demonstrate the robustness of traditional statistical approaches for air quality forecasting and provide a benchmark for future studies incorporating machine learning techniques. The study offers practical value for environmental monitoring and air quality management in Almaty, and similar urban regions.

This study presents a machine learning (ML) framework for predicting heat transfer in magnetohydrodynamic (MHD) mixed convection within a complex V-shaped cavity filled with a nano-enhanced phase change material (NEPCM) suspension. Accurate computational fluid dynamics (CFD) simulations are essential for understanding heat transfer mechanisms in such systems, but generating comprehensive data through high-fidelity models remains computationally expensive. To address this challenge, we develop an integrated ML approach that combines synthetic data generation, physics-informed feature engineering, and optimized ensemble boosting. The methodology first augments a limited 34-sample CFD dataset to 2034 samples using Latin Hypercube Sampling with Radial Basis Function interpolation. Next, 14 physics-based features are engineered to encode the underlying physical phenomena. Finally, hyperparameters of three gradient boosting models—eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost)–are optimized via cross-validation. The framework predicts the average Nusselt number ( N u avg ) and average kinetic energy ( K E avg ) from seven geometric and operational inputs. CatBoost achieved optimal performance for N u avg ( R 2 = 0 . 9745 , mean absolute percentage error = 1.27%), while XGBoost excelled for K E avg ( R 2 = 0 . 9920 , mean absolute percentage error = 1.04%). The novelty of this work lies in its ability to generalize across different output variables and significantly reduce computational cost, enabling rapid design optimization and in-depth parametric analysis. This generalizable approach reduces computation time from hours to milliseconds, facilitating efficient design optimization and in-depth parametric studies. • ML framework predicts heat transfer in MHD mixed convection of NEPCM suspension. • Combines physics-informed feature engineering with optimized ensemble boosting. • CatBoost best for Nusselt number (R 2 = 0.9745), XGBoost for kinetic energy (R 2 = 0.9920). • Synthetic data generation expands limited CFD dataset from 34 to 2034 samples. • Reduces simulation time from hours to milliseconds for efficient design exploration.

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

Air quality, both indoors and outdoors, is crucial for public health as it affects respiratory conditions and overall well-being. Machine learning (ML) techniques offer innovative solutions for monitoring and predicting air quality. However, choosing the right ML algorithms and evaluation metrics is essential for creating accurate air quality prediction models, as these choices directly impact the accuracy and reliability of the results. This study aims to explore and summarize the literature on the use of ML techniques in predicting and optimizing air quality, addressing the urgent issue of air pollution. The review analyzed papers from two electronic databases, namely Scopus and Science Direct. Information on ML techniques for predicting air quality and identifying the main sources of pollution was extracted from 26 studies. The study focuses on common ML techniques employed in air quality prediction, including classification, deep learning, regression, ensemble learning, and combinations of regression and deep learning. It also identifies the evaluation metrics used to assess the performance of these models, such as recall, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). By synthesizing existing knowledge, this study provides a comprehensive understanding of the metrics researchers use to measure the overall effectiveness of ML algorithms. It serves as a benchmark for future research and guides the selection of appropriate evaluation metrics in the field of air quality prediction.

Introduction: Air quality forecasting, particularly predicting Particulate Matter (PM 2.5 ) concentrations, has gained significant attention due to itscritical implications for public health and environmental management.Accurately predicting PM2.5 , a harmful air pollutant associated with respiratory and cardiovascular diseases, is vital for effective air quality management in densely populated urban areas. Materials and methods: This study uses various meteorological and environmental data combinations in Tehran, Iran, this study investigates the efficacy of three predictive modeling techniques Auto Regressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) in forecasting daily and monthly PM2.5levels. The models were evaluated based on performance metrics such as RootMean Square Error (RMSE), Mean Absolute Error (MAE), and R² scores. Results: Results indicate that XGBoost excelled in daily predictions when using solely meteorological data, achieving an R² score of 0.998674, while ARIMA demonstrated strong predictive capacity but struggled with added complexity. LSTM maintained reasonable performance amidst increased data input but faced challenges in both daily and monthly forecasts. Monthly predictions from all models proved less reliable, particularly with ARIMA yielding negative R² values, indicating suboptimal performance compared to simplistic models. Conclusion: The findings highlight the importance of model selection and feature engineering in accurately predicting PM2.5 levels. The study suggests a shift towards hybrid modeling approaches and incorporating diverse environmental data to enhance forecasting accuracy in air quality management, particularly for long-term predictions.

In this study, we explore and compare various time series models to forecast OPEC oil prices, aiming to identify the most accurate model for predicting future trends. The methodology involves the application of multiple time series forecasting models, including Random Walk, Linear Trend, Quadratic Trend, Exponential Trend, S-Curve Trend, Simple Moving Average, Simple Exponential Smoothing, Brown's Linear Exponential Smoothing, Holt's Linear Exponential Smoothing, Brown's Quadratic Exponential Smoothing, and ARIMA. Each model is evaluated based on performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Error (ME), Mean Percentage Error (MPE), Akaike Information Criterion (AIC), Hannan-Quinn Criterion (HQC), and Bayesian Information Criterion (BIC) to determine its accuracy and reliability. Among the models tested, the ARIMA(1,1,0) model emerged as the best-performing model, providing the most accurate predictions. Using this model, we forecast OPEC oil prices for a 12-month period from July 2024 to May 2025. Our findings include detailed forecasts for each month within this period, offering valuable insights for policymakers and market participants. These results highlight the importance of selecting an appropriate time series model to improve forecasting accuracy and inform decision-making processes in the energy sector.

Traffic congestion caused by nonrecurring incidents such as vehicle crashes and debris is a key issue for Traffic Management Centers (TMCs). Clearing incidents in a timely manner is essential to improve safety and reduce delays and emissions for the traveling public. However, TMCs and other responders face a challenge in predicting the duration of incidents (until the roadway is clear), making decisions about what resources to deploy is difficult. To address this problem, this research developed an analytical framework and end-to-end machine learning solution to predict the duration of the incident based on the information available as soon as an incident report is received. Quality predictions of incident duration can help TMCs and other responders take a proactive approach in deploying responder services such as tow trucks, and maintenance crews, or activating alternative routes. The predictions use a combination of classification and regression machine learning modules. The performance of the developed solution has been evaluated based on the Mean Absolute Error (MAE), or deviation from the actual incident duration as well as Area Under the Curve (AUC) and Mean Absolute Percentage Error (MAPE). The results showed that the framework significantly improved the prediction of incident duration compared to previous research methods.

In recent times, an infectious disease namely COVID-19 has affected a large number of individuals. Forecasting models have been helpful in predicting the possible number of confirmed cases, deaths, and recovery counts in the future. In this paper, the prediction of COVID-19 cumulative confirmed cases and deaths for India is analyzed based on various statistical models such as (a) time series, (b) machine learning, and (c) ensemble learning. Autoregressive integrated moving average (ARIMA) and Holt-Winters exponential smoothing in time series; support vector regression (SVR) and linear regression (LR) in machine learning (ML) and random forest regression in ensemble learning (EL) have been implemented for predictions. The accuracies of the trained models are evaluated using metrics such as R-squared value, root mean squared error (RMSE), mean squared error (MSE), mean absolute errors (MAE), and mean absolute percentage error (MAPE). The proposed forecasting models can be used to monitor the rise in COVID-19 cases which can thereby be helpful for government officials to make required changes to their system.KeywordsCOVID-19 pandemic predictionTime-series forecastingEnsemble learningMachine learningSupport vector regression