دورية أكاديمية
Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy.
| العنوان: | Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy. |
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| المؤلفون: | Nikoo MR; Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman. m.reza@squ.edu.om., Zamani MG; Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman., Zadeh MM; Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, USA., Al-Rawas G; Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman., Al-Wardy M; Department of Soils, Water, and Agricultural Engineering, Sultan Qaboos University, Muscat, Oman., Gandomi AH; Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, 2007, Australia. gandomi@uts.edu.au.; University Research and Innovation Center (EKIK), Óbuda University, 1034, Budapest, Hungary. gandomi@uts.edu.au. |
| المصدر: | Scientific reports [Sci Rep] 2024 Jul 16; Vol. 14 (1), pp. 16438. Date of Electronic Publication: 2024 Jul 16. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE |
| أسماء مطبوعة: | Original Publication: London : Nature Publishing Group, copyright 2011- |
| مستخلص: | 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 the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation-reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms. (© 2024. The Author(s).) |
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| معلومات مُعتمدة: | SR/ENG/CAED/22/01 Sultan Qaboos University |
| فهرسة مساهمة: | Keywords: Bayesian maximum entropy-based fusion; Machine learning; Remote sensing; Sentinel-2; Water quality assessment |
| تواريخ الأحداث: | Date Created: 20240716 Latest Revision: 20240719 |
| رمز التحديث: | 20240719 |
| مُعرف محوري في PubMed: | PMC11252294 |
| DOI: | 10.1038/s41598-024-66699-2 |
| PMID: | 39013941 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 2045-2322 |
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| DOI: | 10.1038/s41598-024-66699-2 |