Novel insights into halogenated carbazoles (HCZs) prediction in tap water: a comparative study of grey relational analysis-based neural networks DOI

Qianfeng He,

Wanting Xu, Guolong Chen

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 144482 - 144482

Published: Dec. 1, 2024

Language: Английский

Optimal fixed-time sliding mode control for anti-lock braking systems based fuzzy logic and neural network DOI Creative Commons
Najlae Jennan, El Mehdi Mellouli

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103921 - 103921

Published: Jan. 1, 2025

Language: Английский

Citations

2
Explainable ensemble learning framework for estimating corrosion rate in suspension bridge main cables DOI Creative Commons
Alejandro Jiménez Ríos, Mohamed El Amine Ben Seghier, Vagelis Plevris

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 23, P. 102723 - 102723

Published: Aug. 13, 2024

Language: Английский

Citations

6
Optimization of Amine-Based Carbon Capture: Simulation and Energy Efficiency Analysis of Absorption Section DOI Creative Commons
Amin Hedayati Moghaddam, Morteza Esfandyari, Hossein Sakhaeinia

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103574 - 103574

Published: Nov. 1, 2024

Language: Английский

Citations

6
Urban vegetation benefits in mediterranean cities for climate change adaptation and water usage efficiency – a case study in Algarve, Portugal DOI Creative Commons
Pedro Matias, Manuela Moreira da Silva,

João Teigão

et al.

Frontiers in Environmental Science, Journal Year: 2025, Volume and Issue: 13

Published: Feb. 6, 2025

In the current climate change scenario, Mediterranean cities face heatwaves and reduced availability of freshwater alternated with intense precipitation events in short periods. The demand for water, especially urban tourism purposes, is rising, particularly coastal cities. importance integrating nature into cities, trees, has been studied its benefits adapting to improving quality life. However, water scarcity remains a limiting factor ensuring needs trees and, consequently, ecosystem services they provide. this study, we quantified (CO 2 sequestration storage, O production, air pollutants removal, hydrological effects as transpiration, intercepted avoided runoff) provided by vegetation Faro (Algarve, Portugal), detailed analysis three green spaces. We analyzed given spaces community preferences. Rainwater harvesting was an alternative source irrigation advantages cycle. found that across city sequesters 1.09 × 10 3 t. yr −1 CO , stores 4.01 t C, contributes pollutant removal = 114; 3.56 ; NO 313; SO 224; PM 872; 2.5 70) kg. prevents 861 m . surface runoff. general, people inquired use visit value city’s spaces, enjoy activities nature, have definite preferences regarding are available suggest actions improve these It confirmed order maintain their provides community, rainwater utmost relevance representing reduction drinking 4.20

Language: Английский

Citations

0
Development of Machine Learning-Aided Rapid CFD Prediction for Optimal Urban Wind Environment Design DOI
Aiymzhan Baitureyeva, Tong Yang, Hua Sheng Wang

et al.

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: 121, P. 106208 - 106208

Published: Feb. 16, 2025

Language: Английский

Citations

0
A Deep Convolutional Generative Adversarial Network (DCGAN) for the fast estimation of pollutant dispersion fields in indoor environments DOI Creative Commons
Claudio Alanis Ruiz, M.G.L.C. Loomans, T. van Hooff

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112856 - 112856

Published: March 1, 2025

Language: Английский

Citations

0
Forecasting Used Car Prices Using Machine Learning DOI Creative Commons

Eni Khusnul Khotimah,

Dwiretno Istiyadi Swasono,

Gama Wisnu Fajarianto

et al.

IT JOURNAL RESEARCH AND DEVELOPMENT, Journal Year: 2025, Volume and Issue: 9(2), P. 123 - 139

Published: March 12, 2025

In an increasingly competitive era, it is crucial for car dealers and retailers to address the challenges of accurately determining prices used cars. To tackle these challenges, this study implements Machine Learning models predict accurately. By applying Artificial Neural Network (ANN) Random Forest Regression algorithms, research aims evaluate performance methods in predicting prices. The price data was obtained from Kaggle repository, consisting 14,657 entries that provide comprehensive information about analysis focuses on six main columns, including Brand, Model, Variant, Year, Mileage, estimate Model evaluation conducted using Mean Absolute Error (MAE) as primary metric. results show ANN model achieved a lower MAE (0.035) compared (0.047), indicating better These findings demonstrate effectiveness handling complexity non-linear relationships between variables involved forecasting Additionally, contributes implementation more accurate predictions, enabling automotive companies improve operational efficiency greater benefits community.

Language: Английский

Citations

0
Artificial Neural Network for Air Pollutant Concentration Predictions Based on Aircraft Trajectories over Suvarnabhumi International Airport DOI Creative Commons
Patcharin Kamsing, Chunxiang Cao, Wuttichai Boonpook

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 366 - 366

Published: March 24, 2025

Air pollutant concentration prediction is essential not only for effective air quality management but also planning aircraft and ground vehicle route networks in terminal areas. In this work, an artificial neural network (ANN) used to predict the levels of four types pollutants (CO, NO2, PM2.5, PM10) at Suvarnabhumi International Airport. By leveraging Automatic Dependent Surveillance-Broadcast (ADS-B) historical data, trajectory pattern clustering implemented by using K-means Gaussian mixture model (GMM) algorithms. Then, those patterns are inputted together with other flight data into ANN computation processes, resulting each kind focus pollutant. The results demonstrate that mean square errors (MSEs) predicted models CO PM2.5 have acceptable values 51.7622 53.9682, respectively, while NO2 PM10 has MSEs 139.6674 124.2517, respectively. This study contributes advancement methodologies, facilitating better decision-making proactive management, airports. Although some focused slightly high MSEs, further needed enhance capacity.

Language: Английский

Citations

0
Novel feature selection based ANN for optimal solar panels tilt angles prediction in micro grid DOI Creative Commons
Amit Kumar Yadav, Amit Kumar Yadav, Ashwani Kumar

et al.

Case Studies in Thermal Engineering, Journal Year: 2024, Volume and Issue: 61, P. 104853 - 104853

Published: July 17, 2024

Predicting PV system electricity output is necessary for daily operational management and annual power planning when integrating solar collector-based photovoltaic (PV) stations into micro grids. Tilting the panels at ideal angle to maximize energy capture station production. This optimal tilt (OTA) must be predicted as it a nonlinear function of total radiation, diffuse direct radiation. research explores use feature selection-based artificial neural networks (ANN) with various machine learning algorithms predict OTA systems specific locations, aiming in The study identifies global clarity index, radiation on inclined surfaces most critical inputs predicting OTA, while extraterrestrial deemed least significant. Implementing appropriate input variables significantly enhanced prediction accuracy from 38.59 % 90.72 %. Among evaluated, Elman network demonstrated greatest improvement.

Language: Английский

Citations

3
Error prediction for machining thin-walled blade with Kriging model DOI Creative Commons

Jinhua Zhou,

Sitong Qian,

Tong‐Seok Han

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104645 - 104645

Published: March 1, 2025

Language: Английский

Citations

0