Advanced Air Quality Forecasting Using an Enhanced Temporal Attention-Driven Graph Convolutional Long Short-Term Memory Model with Seasonal-Trend Decomposition DOI Creative Commons

Yuvaraja Boddu,

Manimaran Aridoss, Arunkumar Balakrishnan

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 189233 - 189252

Published: Jan. 1, 2024

Language: Английский

Groundwater for drinking and sustainable agriculture and public health hazards of nitrate: developmental and sustainability implications for an arid aquifer system DOI Creative Commons
Boualem Bouselsal,

Adel Satouh,

Johnbosco C. Egbueri

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104160 - 104160

Published: Jan. 1, 2025

Language: Английский

Citations

5

Enhanced Prediction of Energy Dissipation Rate in Hydrofoil-Crested Stepped Spillways Using Novel Advanced Hybrid Machine Learning Models DOI Creative Commons
Ehsan Afaridegan,

Nosratollah Amanian

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 103985 - 103985

Published: Jan. 1, 2025

Language: Английский

Citations

2

Geospatial digital mapping of soil organic carbon using machine learning and geostatistical methods in different land uses DOI Creative Commons
Yahya Parvizi, Shahrokh Fatehi

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

Published: Feb. 6, 2025

Improper management of soil resources leads to the destruction organic carbon (SOC) stock and, as a result, reduction quality, well accelerating process climate change through release SOC into atmosphere. This study was conducted evaluate potential different simulation models map spatial variability affected by land use in area Qarasu watershed Kermanshah province, west Iran. Map sampling points prepared using Latin hypercube method. A total 168 observation were selected and profile dug described these points. The samples taken horizon determine content laboratory. mapped kriging geostatistical method area. changes simulated multivariate analysis machine learning methods including generalized linear model (GLM), additive (LAM), cubist, random forest (RF), support vector (SVM) models. Comprehensive measurement data is utilized develop validate predictive Predictor variables included 16 topographic two vegetation, six parent material, four climatic variables. In-depth statistical analyses are proposed performance. results showed that ranged from 0.19 8.44 percent uses. spherical variogram with MAE = 0.41 best fits interpolate ordinary LAM estimated wider range (SOC 0.18–4.82%) among model. However, RF (R2 0.64 RMSE 0.58%) most accurate predicting quantity comparing other It can be used reliable predict similar semiarid regions West Asia Among predictor variables, material's intrinsic properties topography had greatest effect variability.

Language: Английский

Citations

2

A Study on the water resource assets management audit for outgoing officials considering asset elements DOI
Shuqin Li,

Juqin Shen,

Fuhua Sun

et al.

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144931 - 144931

Published: Feb. 1, 2025

Language: Английский

Citations

2

Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models DOI Creative Commons
Khabat Khosravi, Aitazaz A. Farooque, Seyed Amir Naghibi

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 85, P. 102933 - 102933

Published: Dec. 7, 2024

Language: Английский

Citations

11

Enhancing prediction of dissolved oxygen over Santa Margarita River: Long short-term memory incorporated with multi-objective observer-teacher-learner optimization DOI
Siyamak Doroudi, Yusef Kheyruri, Ahmad Sharafati

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 70, P. 106969 - 106969

Published: Jan. 11, 2025

Language: Английский

Citations

1

Evaluating empirical and machine learning approaches for reference evapotranspiration estimation using limited climatic variables in Nepal DOI Creative Commons

Erica Shrestha,

Suyog Poudyal,

Anup Ghimire

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104254 - 104254

Published: Feb. 1, 2025

Language: Английский

Citations

1

Impact of Nutrient Dynamics on Chlorophyll-a Concentrations in Non-Interconnected Lakes: A Study in the Vellore and Chennai Region DOI Creative Commons

Mageswaran Raghul,

P. Porchelvan

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104315 - 104315

Published: Feb. 1, 2025

Language: Английский

Citations

1

Integrating piecewise and symbolic regression with remote sensing data for spatiotemporal analysis of surface water total dissolved solids in the Karun River, Iran DOI Creative Commons
Javad Zahiri, Mohammad Reza Nikoo,

Adell Moradi-Sabzkouhi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104159 - 104159

Published: Jan. 1, 2025

Language: Английский

Citations

0

Enhanced accuracy and interpretability of nitrous oxide emission prediction of wastewater treatment plants through machine learning of univariate time series: A novel approach of learning feature reconstruction DOI
Zixuan Wang, Anlei Wei, K.S. Tang

et al.

Journal of Water Process Engineering, Journal Year: 2025, Volume and Issue: 71, P. 107263 - 107263

Published: Feb. 15, 2025

Language: Английский

Citations

0