Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(60), P. 126116 - 126131
Published: Nov. 27, 2023
Language: Английский
Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(60), P. 126116 - 126131
Published: Nov. 27, 2023
Language: Английский
Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)
Published: July 16, 2024
Abstract In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting demand for ample, superior downstream proves to be a formidable task. Thus, accurately estimating and mapping indicators (WQIs) paramount sustainable planning of inland in study area. Since traditional procedures collect data time-consuming, labor-intensive, costly, resources management has shifted gathering field measurement utilizing remote sensing (RS) data. WDD been threatened various driving forces recent years, such as contamination different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, microbial contamination. Therefore, this aimed retrieve map WQIs, namely dissolved oxygen (DO) chlorophyll-a (Chl-a) (WDD) reservoir Sentinel-2 (S2) satellite using new procedure weighted averaging, Bayesian Maximum Entropy-based Fusion (BMEF). To do so, outputs four Machine Learning (ML) algorithms, Multilayer Regression (MLR), Random Forest (RFR), Support Vector (SVRs), XGBoost, were combined approach together, considering uncertainty. Water samples 254 systematic plots obtained (T), electrical conductivity (EC), (Chl-a), pH, oxidation–reduction potential (ORP), WDD. The findings indicated that, throughout both training testing phases, BMEF model outperformed individual machine learning models. Considering Chl-a, WQI, R-squared, evaluation indices, MLR, SVR, RFR, XGBoost 6%, 9%, 2%, 7%, respectively. Furthermore, results significantly enhanced when best combination spectral bands was considered estimate specific WQIs instead all S2 input variables ML algorithms.
Language: Английский
Citations
8Chemical Engineering Science, Journal Year: 2025, Volume and Issue: unknown, P. 121293 - 121293
Published: Jan. 1, 2025
Language: Английский
Citations
1Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 124971 - 124971
Published: March 20, 2025
Language: Английский
Citations
1Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: 38(8), P. 3177 - 3198
Published: May 13, 2024
Language: Английский
Citations
6Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 364, P. 121466 - 121466
Published: June 12, 2024
Language: Английский
Citations
6Buildings, Journal Year: 2024, Volume and Issue: 14(3), P. 641 - 641
Published: Feb. 29, 2024
Unconfined compressive strength (UCS) is an important parameter of rock and soil mechanical behavior in foundation engineering design construction. In this study, salinized frozen selected as the research object, GDS tests, ultrasonic scanning electron microscopy (SEM) tests are conducted. Based on classification method model parameters, 2 macroscopic 38 mesoscopic 19 microscopic parameters selected. A machine learning used to predict considering three-level characteristic parameters. Four accuracy evaluation indicators evaluate six models. The results show that radial basis function (RBF) has best UCS predictive performance for both training testing stages. terms acceptable stability loss, through analysis gray correlation rough set total amount proportion optimized so there 2, 16, 16 macro, meso, micro a sequence, respectively. simulation aforementioned models with RBF still performs optimally. addition, after optimization, sensitivity third-level more reasonable. proved be effective predicting UCS. This study improves prediction ability by classifying optimizing provides useful reference future salty seasonally regions.
Language: Английский
Citations
5Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102665 - 102665
Published: June 24, 2024
Language: Английский
Citations
4Ecotoxicology and Environmental Safety, Journal Year: 2024, Volume and Issue: 289, P. 117499 - 117499
Published: Dec. 12, 2024
Language: Английский
Citations
4The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 954, P. 176256 - 176256
Published: Sept. 20, 2024
Language: Английский
Citations
3Stochastic Environmental Research and Risk Assessment, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 28, 2025
Language: Английский
Citations
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