Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 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.

Язык: Английский

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The decontaminant mechanism of polyamide membranes for sulfamethoxazole: The insights from combined machine learning and molecular modelling

Chemical Engineering Science, Год журнала: 2025, Номер unknown, С. 121293 - 121293

Опубликована: Янв. 1, 2025

Язык: Английский

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Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman

Journal of Environmental Management, Год журнала: 2025, Номер 380, С. 124971 - 124971

Опубликована: Март 20, 2025

Язык: Английский

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Discharge coefficient estimation of modified semi-cylindrical weirs using machine learning approaches

Stochastic Environmental Research and Risk Assessment, Год журнала: 2024, Номер 38(8), С. 3177 - 3198

Опубликована: Май 13, 2024

Язык: Английский

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Research on machine learning hybrid framework by coupling grid-based runoff generation model and runoff process vectorization for flood forecasting

Journal of Environmental Management, Год журнала: 2024, Номер 364, С. 121466 - 121466

Опубликована: Июнь 12, 2024

Язык: Английский

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Prediction of the Unconfined Compressive Strength of Salinized Frozen Soil Based on Machine Learning

Buildings, Год журнала: 2024, Номер 14(3), С. 641 - 641

Опубликована: Фев. 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.

Язык: Английский

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Pavement raveling inspection using a new image texture-based feature set and artificial intelligence

Advanced Engineering Informatics, Год журнала: 2024, Номер 62, С. 102665 - 102665

Опубликована: Июнь 24, 2024

Язык: Английский

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Evaluation and source identification of water pollution

Ecotoxicology and Environmental Safety, Год журнала: 2024, Номер 289, С. 117499 - 117499

Опубликована: Дек. 12, 2024

Язык: Английский

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Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland

The Science of The Total Environment, Год журнала: 2024, Номер 954, С. 176256 - 176256

Опубликована: Сен. 20, 2024

Язык: Английский

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Improving the accuracy of daily runoff prediction using informer with black kite algorithm, variational mode decomposition, and error correction strategy

Stochastic Environmental Research and Risk Assessment, Год журнала: 2025, Номер unknown

Опубликована: Фев. 28, 2025

Язык: Английский

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