Experimental and artificial intelligence approaches to measuring the wear behavior of DIN St28 steel boronized by the box boronizing method using a mechanically alloyed powder source DOI
M. Gökhan Albayrak, Ertan Evi̇n, Oktay Yi̇ği̇t

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 120, P. 105910 - 105910

Published: Jan. 30, 2023

Language: Английский

Temporal Variation and Spatial Distribution in the Water Environment Helps Explain Seasonal Dynamics of Zooplankton in River-Type Reservoir DOI Open Access
Jingyun Yin,

Jihong Xia,

Zhichang Xia

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(21), P. 13719 - 13719

Published: Oct. 22, 2022

Integrated assessment of the water environment has become widespread in many rivers, lakes, and reservoirs; however, aquatic organisms freshwater are often overlooked this process. Zooplankton, as primary consumers, sensitive responsive to changes environment. Water zooplankton samples were collected on-site at Shanxi Reservoir quarterly determine 12 environmental indicators quantify abundance Cladocera, Copepoda Rotifera by using ZooScan image-scanning analysis system, combined with OLYMPUS BX51 machine learning recognition classification. The aim was explore relationship between factors through their spatial temporal heterogeneity. Through principal component analysis, redundancy cluster variations driving population growth different seasons could be identified. At same time, taxa can form clusters related during abundant period summer dry winter. Based on long-term monitoring, used a comprehensive indicator for ecological health evaluation, well providing scientific support regional resources deployment management.

Language: Английский

Citations

14

Assessment of total evaporation rates and its surface distribution by tridimensional modelling and remote sensing DOI
Sofia Midauar Gondim Rocha,

Ernesto Molinas,

Ítalo Sampaio Rodrigues

et al.

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 327, P. 116846 - 116846

Published: Nov. 28, 2022

Language: Английский

Citations

14

Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability? DOI Creative Commons
Chen Zhang, Tianyu Fu

Geoscientific model development, Journal Year: 2023, Volume and Issue: 16(14), P. 4315 - 4329

Published: July 28, 2023

Abstract. Autocalibration techniques have the potential to enhance efficiency and accuracy of intricate process-based hydrodynamic water quality models. In this study, we developed a new R-based autocalibration toolkit for Environmental Fluid Dynamics Code (EFDC) implemented it into recalibration Yuqiao Reservoir Water Quality Model (YRWQM), with long-term observations from 2006 2015, including dry, normal, wet years. The facilitated contributed exploring how model recalibrated performs more accurately robustly. Previously, original YRWQM was calibrated validated dry years in 2007, respectively. Compared YRWQM, performed just as well surface elevation, Kling–Gupta (KGE) 0.99, temperature, KGE 0.91, while performing better modeling total phosphorus (TP), chlorophyll (Chl a), dissolved oxygen (DO), KGEs 0.10, 0.30, 0.74, Furthermore, improved by 43 %–202 % TP–Chl a–DO process when compared models only overestimated DO concentrations, probably explained parameter algal growth rate that increased 84 %. poorly Chl a, due 50 reduction carbon-to-chlorophyll ratio, triggered changes composition population. Our study suggests calibrating may be an important measure improve robustness under severe hydrological variability. newly general automatic calibration possible hierarchical strategy will also powerful tool future complex calibration.

Language: Английский

Citations

8

Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth DOI Creative Commons
Whitney M. Woelmer, R. Quinn Thomas, Freya Olsson

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 83, P. 102825 - 102825

Published: Sept. 11, 2024

Language: Английский

Citations

3

Experimental and artificial intelligence approaches to measuring the wear behavior of DIN St28 steel boronized by the box boronizing method using a mechanically alloyed powder source DOI
M. Gökhan Albayrak, Ertan Evi̇n, Oktay Yi̇ği̇t

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2023, Volume and Issue: 120, P. 105910 - 105910

Published: Jan. 30, 2023

Language: Английский

Citations

7