Results 61 to 70 of about 1,524 (177)

Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods

Journal of Air Pollution and Health, 2020
Introduction: Estimating air pollution levels in areas with no measurements is a major concern in health-related studies. Therefore, the aim of this study was to investigate the amount of exposure to particulate matter below 2.5 μ (PM2.5) in the ...
Kazhal Masroor, Farzad Fanaei, Somayeh Yousefi, Mohammad Raeesi, Hossein Abbaslou, Abbas Shahsavani, Mostafa Hadei   +6 more
doaj   +1 more source

Sparse polynomial chaos expansion for universal stochastic kriging

Journal of Computational and Applied Mathematics
Surrogate modelling techniques have opened up new possibilities to overcome the limitations of computationally intensive numerical models in various areas of engineering and science. However, while fundamental in many engineering applications and decision-making, the incorporation of uncertainty quantification into meta-models remains a challenging ...
J.C. García-Merino, C. Calvo-Jurado, E. García-Macías   +2 more
openaire   +4 more sources

Response of Vegetation Greenness to Extreme Droughts and Possible Mechanisms in Guizhou Province, China

Ecology and Evolution, Volume 16, Issue 1, January 2026.
Spatiotemporal variations of the two drought events and the different response of vegetation NDVI to both events were investigated based on multiple data in Guizhou Province. The 2009–2010 drought exerted a inhibitory effect on vegetation growth, while the 2011 drought exhibited a milder impact, even demonstrating continued green growth amidst the dry ...
Chuncan Meng, Yingqing Cen, Xu Xue
wiley   +1 more source

Mathematical aspects of the kriging applied on landslide in Halenkovice (Czech Republic)

Open Geosciences, 2016
Kriging is one of the geostatistical techniques for spatial data analysis that is usually used for a modelling of natural phenomena or a creation of digital elevation models.
Zůvala Robert, Fišerová Eva, Marek Lukáš   +2 more
doaj   +1 more source

Free‐access geospatial data and machine learning deliver high‐resolution soil salinity maps in the Sonoran Basin and Range

Vadose Zone Journal, Volume 25, Issue 1, January/February 2026.
Abstract Soil salinity is a serious threat to crop productivity and is anticipated to increase in the coming decades, particularly in semi‐arid to arid agricultural regions. Accessible geospatial data and data mining techniques can enable high‐resolution mapping of soil salinity to improve predictions and soil management.
Milton Valencia‐Ortiz, Amanda J. Ashworth, Hans Edwin Winzeler, Joshua M. Blackstock, Marcelo Mancini, Rebecca L. Muenich, Phillip R. Owens   +6 more
wiley   +1 more source

Investigation of relationship between particulate matter (PM2.5) and meteorological parameters in Isfahan, Iran

Journal of Air Pollution and Health, 2020
Introduction: Estimating air pollution levels in areas with no measurements is a major concern in health-related studies. Therefore, the aim of this study was to investigate the amount of exposure to particulate matter below 2.5 µ (PM2.5) in the ...
Majid Kermani, Ahmad Jonidi Jafari, Mitra Gholami Mitra Gholami, Mahdi Farzadkia, Hossein Arfaeinia, Abbas Shahsavani, Abbas Norouzian, Mohsen Dowlati, Farzad Fanaei   +8 more
doaj   +1 more source

Spatial analyses of groundwater levels using universal kriging

Journal of Earth System Science, 2007
For water levels, generally a non-stationary variable, the technique of universal kriging is applied in preference to ordinary kriging as the interpolation method. Each set of data in every sector can fit different empirical semivariogram models since they have different spatial structures.
Gundogdu, KEMAL SULHİ, Guney, Ibrahim
openaire   +4 more sources

A Computationally Efficient Stochastic Method for Quantifying the Effects of Multi‐Surrogate Model Uncertainty on Saltwater Remediation Optimization

Water Resources Research, Volume 62, Issue 1, January 2026.
Abstract Machine learning models are highly potential to substitute computationally intensive numerical simulation models in saltwater intrusion (SWI) remediation optimization. However, uncertainty inherent in machine learning models can propagate through predictions into optimization, resulting in inaccurate solutions.
Yulu Huang, Jina Yin, Chunhui Lu
wiley   +1 more source

A Gaussian Process Regression Framework for Enhancing Convergence in Power System State Estimation

IET Generation, Transmission &Distribution, Volume 20, Issue 1, January/December 2026.
1. This research introduces a hybrid state estimator that integrates Gaussian Process Regression (GPR) with the traditional Weighted Least Squares (WLS) method for power networks. 2. The method uses GPR to create virtual measurements, which significantly reduces iteration counts and accelerates computation time in benchmarks. 3.
Juan M. Ramirez, Ricardo Galarza‐Villa
wiley   +1 more source

From Empirical Models to Physics‐Informed Neural Networks: The Evolution of Oil Production Forecasting

Journal of GeoEnergy, Volume 2026, Issue 1, 2026.
Production forecasting for oil and gas wells is a decisive element of field‐development planning because it directly guides recovery strategy design, production optimisation and risk management. Conventional methods, including empirical decline‐curve analysis (DCA) and full‐physics numerical simulation, are limited either by their inability to capture ...
Shitan Yin, Erlong Yang, Xianjun Wang, Chi Dong, Mehdi Ostadhassan   +4 more
wiley   +1 more source