Artificial Intelligence Technologies for Forecasting Air Pollution and Human Health: A Narrative Review

Sustainability, Journal Year: 2022, Volume and Issue: 14(16), P. 9951 - 9951

Published: Aug. 11, 2022

Air pollution is a major issue all over the world because of its impacts on environment and human beings. The present review discussed sources pollutants environmental health current research status forecasting techniques in detail; this study presents detailed discussion Artificial Intelligence methodologies Machine learning (ML) algorithms used early-warning systems; moreover, work emphasizes more (particularly Hybrid models) for various (e.g., PM2.5, PM10, O3, CO, SO2, NO2, CO2) focus given to AI ML predicting chronic airway diseases prediction climate changes heat waves. hybrid model has better performance than single models it greater accuracy warning systems. evaluation error indexes like R2, RMSE, MAE MAPE were highlighted based models.

Language: Английский

---

Automated waste-sorting and recycling classification using artificial neural network and features fusion: a digital-enabled circular economy vision for smart cities

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 82(25), P. 39617 - 39632

Published: July 28, 2022

Language: Английский

---

Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 1, 2025

This paper provides a novel approach to estimating CO₂ emissions with high precision using machine learning based on DPRNNs NiOA. The data preparation used in the present methodology involves sophisticated stages such as Principal Component Analysis (PCA) well Blind Source Separation (BSS) reduce noise improve feature selection. purified input dataset is model, where both short and long-term temporal dependencies are captured well. NiOA utilized tune those parameters; result, prediction accuracy quite spectacular. Experimental results also demonstrate that proposed NiOA-DPRNNs framework gets highest value of R2 (0.9736), lowest error rates fitness values than other existing models optimization methods. From Wilcoxon ANOVA analyses, one can approve specificity consistency findings. Liebert Ruple firmly rethink this rather simple output robust theoretic empirical for evaluating projecting CO2 emissions; they view it helpful guide policymakers fighting global warming. Further study build up theory include greenhouse gases create methods enabling instantaneous tracking responsive approaches.

Language: Английский

---

Transmission Probability of SARS-CoV-2 in Office Environment Using Artificial Neural Network

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 121204 - 121229

Published: Jan. 1, 2022

In this paper, curve-fitting and an artificial neural network (ANN) model were developed to predict R-Event. Expected number of new infections that arise in any event occurring over a total time space is termed as Real-time data for the office environment was gathered spring 2022 naturally ventilated room Roorkee, India, under composite climatic conditions. To ascertain merit proposed ANN models, performances approach compared against curve fitting regarding conventional statistical indicators, i.e., correlation coefficient, root mean square error, absolute Nash-Sutcliffe efficiency index, percentage a20-index. Eleven input parameters namely indoor temperature ( T_In ), relative humidity xmlns:xlink="http://www.w3.org/1999/xlink">RH_In area opening xmlns:xlink="http://www.w3.org/1999/xlink">A_O occupants xmlns:xlink="http://www.w3.org/1999/xlink">O per person xmlns:xlink="http://www.w3.org/1999/xlink">A_P volume xmlns:xlink="http://www.w3.org/1999/xlink">V_P xmlns:xlink="http://www.w3.org/1999/xlink">CO ₂ concentration air quality index xmlns:xlink="http://www.w3.org/1999/xlink">AQI outer wind speed xmlns:xlink="http://www.w3.org/1999/xlink">W_S outdoor xmlns:xlink="http://www.w3.org/1999/xlink">T_Out xmlns:xlink="http://www.w3.org/1999/xlink">RH_Out ) used study R-Event value output. The primary goal research establish link between value; eventually providing prediction purposes. case study, coefficient 0.9992 0.9557, respectively. It shows model's higher accuracy than prediction. Results indicate performance (R=0.9992, RMSE=0.0018708, MAE=0.0006675, MAPE=0.8643816, NS=0.9984365, a20-index=0.9984300) reliable highly accurate R-event offices.

Language: Английский

---

Towards Federated Learning and Multi-Access Edge Computing for Air Quality Monitoring: Literature Review and Assessment

Sustainability, Journal Year: 2023, Volume and Issue: 15(18), P. 13951 - 13951

Published: Sept. 20, 2023

Systems for monitoring air quality are essential reducing the negative consequences of pollution, but creating real-time systems encounters several challenges. The accuracy and effectiveness these can be greatly improved by integrating federated learning multi-access edge computing (MEC) technology. This paper critically reviews state-of-the-art methodologies MEC-enabled systems. It discusses immense benefits learning, including privacy-preserving model training, MEC, such as reduced latency response times, applications. Additionally, it highlights challenges requirements developing implementing systems, data quality, security, privacy, well need interpretable explainable AI-powered models. By leveraging advanced techniques technologies, overcome various deliver accurate, reliable, timely predictions. Moreover, this article provides an in-depth analysis assessment emphasizes further research to develop more practical affordable decentralized with performance security while ensuring ethical responsible use support informed decision making promote sustainability.

Language: Английский

---

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 55, P. 101895 - 101895

Published: Jan. 1, 2023

Language: Английский

---

Development of a Reliable Machine Learning Model to Predict Compressive Strength of FRP-Confined Concrete Cylinders

Buildings, Journal Year: 2023, Volume and Issue: 13(4), P. 931 - 931

Published: March 31, 2023

The degradation of reinforced concrete (RC) structures has raised major concerns in the industry. demolition existing shown to be an unsustainable solution and leads many financial concerns. Alternatively, strengthening sector put forward sustainable solutions, such as retrofitting rehabilitation structural elements with fiber-reinforced polymer (FRP) composites. Over past four decades, FRP retrofits have attracted attention from scientific community, thanks their numerous advantages having less weight, being non-corrodible, etc., that help enhance axial, flexural, shear capacities RC members. This study focuses on predicting compressive strength (CS) FRP-confined cylinders using analytical models machine learning (ML) models. To achieve this, a total 1151 specimens been amassed comprehensive literature studies. ML utilized are Gaussian process regression (GPR), support vector (SVM), artificial neural network (ANN), optimized SVM, GPR input parameters used for prediction include geometrical characteristics specimens, mechanical properties composite, CS concrete. results five compared nineteen evaluated algorithms imply model found best among all other models, demonstrating higher correlation coefficient, root mean square error, absolute percentage a-20 index, Nash–Sutcliffe efficiency values 0.9960, 3.88 MPa, 3.11%, 2.17 0.9895, 0.9921, respectively. R-value is 0.37%, 0.03%, 5.14%, 2.31% than ANN, GPR, SVM respectively, whereas error value is, 81.04%, 12.5%, 471.77%, 281.45% greater model.

Language: Английский

---

Environmentally Friendly Concrete Compressive Strength Prediction Using Hybrid Machine Learning

Sustainability, Journal Year: 2022, Volume and Issue: 14(20), P. 12990 - 12990

Published: Oct. 11, 2022

In order to reduce the adverse effects of concrete on environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically environmentally sustainable alternative portland cement. This is accomplished through utilization alumina-silicate waste materials as a cementitious binder. These synthesized by activating minerals with alkali. paper employs three-step machine learning (ML) approach in estimate compressive strength geopolymer concrete. The ML methods include CatBoost regressors, extra trees gradient boosting regressors. addition 84 experiments literature, 63 were constructed tested. Using Python language programming, models built from 147 samples four variables. Three these combined using blending technique. Model performance was evaluated several metric indices. Both individual hybrid predict high accuracy. However, model claimed able improve prediction accuracy 13%.

Language: Английский

---

Prognosis of compressive strength of fly‐ash‐based geopolymer‐modified sustainable concrete with ML algorithms

Structural Concrete, Journal Year: 2022, Volume and Issue: 24(3), P. 3990 - 4014

Published: Sept. 14, 2022

Abstract Sustainable concrete is the demand of present era to reduce carbon emissions. Fly‐ash‐based geopolymer (FLAG) has been used in construction industry for more than one and a half decades. The compressive strength (CS) plays crucial role mechanical properties concrete. Laboratory experiments take huge amount time cost estimate CS Although analytical methods exist concrete, but these models cannot forecast with better precision due complexity design mixes. machine learning (ML)‐based have helpful estimating high accuracy reliability. In this article, four ML algorithms (support vector [SVM], linear regression [LR], ensemble [EL], Gaussian process [GPR]) three optimized (EL, SVM, GPR) FLAG R ‐value LR, EL, SVMR, GPR, SVMR GPR are 0.8916, 0.9172, 0.9313, 0.9529, 0.9459, 0.9348 0.9590, respectively. model an 0.9590 RMSE value 1.7132 MPa outperformed all other models. performances developed illustrated through Taylor diagram error plot. feature importance input parameters explained explainable technique. developed, can be reliable tool greater also reducing cost.

Language: Английский

---

Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms

Materials, Journal Year: 2022, Volume and Issue: 15(23), P. 8295 - 8295

Published: Nov. 22, 2022

The bond strength between concrete and corroded steel reinforcement bar is one of the main responsible factors that affect ultimate load-carrying capacity reinforced (RC) structures. Therefore, prediction accurate has become an important parameter for safety measurements RC However, analytical models are not enough to estimate strength, as they built using various assumptions limited datasets. machine learning (ML) techniques named artificial neural network (ANN) support vector (SVM) have been used bar. considered input parameters in this research surface area specimen, cover, type bars, yield compressive diameter length, water/cement ratio, corrosion level bars. These were build ANN SVM models. reliability developed compared with twenty Moreover, analyzed results revealed precision efficiency higher radar plot Taylor diagrams also utilized show graphical representation best-fitted model. proposed model best model, a correlation coefficient 0.99, mean absolute error 1.091 MPa, root square 1.495 MPa. Researchers designers can apply precisely steel-to-concrete strength.

Language: Английский

---