Polycyclic aromatic hydrocarbons (PAHs) in the surficial sediments of the Abadan freshwater resources − Northwest of the Persian Gulf

Haghnazar, Hamed; Abbasi, Yasaman; Morovati, Reza; Johannesson, Karen H.; Somma, Renato; Pourakbar, Mojtaba; Aghayani, Ehsan

doi:10.1016/j.gexplo.2024.107390

Cited by 4 publications

(1 citation statement)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our contribution can help in extracting the precise shape and area of a water body which can further be used to measure the expansion or shrinking of a water body over a period of time. This can be crucial as water body extraction is an important task in different disciplines, such as lake coastal zone management, coastline change, and erosion monitoring, flood prediction, climate and environmental change, and evaluation of water resources (Haghnazar et al, 2024;Hosseinzadeh et al, 2023Hosseinzadeh et al, , 2024Jiang et al, 2018). Timely monitoring of surface water and delivering data on the dynamics of surface water are also essential for policy and decision-making processes (Sarp & Ozcelik, 2017).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Filali Boubrahimi,

Neema,

Nassar

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

With increasing demands for precise water resource management, there is a growing need for advanced techniques in mapping water bodies. The currently deployed satellites provide complementary data that are either of high spatial or high temporal resolutions. As a result, there is a clear trade‐off between space and time when considering a single data source. For the efficient monitoring of multiple environmental resources, various Earth science applications need data at high spatial and temporal resolutions. To address this need, many data fusion methods have been described in the literature, that rely on combining data snapshots from multiple sources. Traditional methods face limitations due to sensitivity to atmospheric disturbances and other environmental factors, resulting in noise, outliers, and missing data. This paper introduces Hydrological Generative Adversarial Network (Hydro‐GAN), a novel machine learning‐based method that utilizes modified GANs to enhance boundary accuracy when mapping low‐resolution MODIS data to high‐resolution Landsat‐8 images. We propose a new non‐saturating loss function for the Hydro‐GAN generator, which maximizes the log of discriminator probabilities to promote stable updates and aid convergence. By focusing on reducing squared differences between real and synthetic images, our approach enhances training stability and overall performance. We specifically focus on mapping water bodies using MODIS and Landsat‐8 imagery due to their relevance in water resource management tasks. Our experimental results demonstrate the effectiveness of Hydro‐GAN in generating high‐resolution water body maps, outperforming traditional methods in terms of boundary accuracy and overall quality.

show abstract

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Filali Boubrahimi,

Neema,

Nassar

et al. 2024

Water Resources Research

View full text Add to dashboard Cite

show abstract

Polycyclic aromatic hydrocarbons in ship breaking area and associated ecological risk assessment: evidence from the Sitakund ship-breaking area in Bangladesh

Barua,

Mutsuddi,

Sultana

et al. 2024

Environ Sci Pollut Res

View full text Add to dashboard Cite

Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

Morovati,

Kisi

2024

Hydrology

Self Cite

View full text Add to dashboard Cite

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran’s Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions.

show abstract

Polycyclic aromatic hydrocarbons (PAHs) in the surficial sediments of the Abadan freshwater resources − Northwest of the Persian Gulf

Cited by 4 publications

References 55 publications

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Spatiotemporal Data Augmentation of MODIS‐Landsat Water Bodies Using Adversarial Networks

Polycyclic aromatic hydrocarbons in ship breaking area and associated ecological risk assessment: evidence from the Sitakund ship-breaking area in Bangladesh

Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

Contact Info

Product

Resources

About