Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning DOI Creative Commons
Zhixin Wang,

Zhenqi Zhang,

Hailong Li

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1742 - 1742

Опубликована: Окт. 3, 2024

Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need study water quality. The use remote sensing for quality inversion offers a precise, timely, comprehensive way evaluate present state future trajectories In this paper, model utilizing machine learning was developed variations in Ma’an Archipelago Marine Special Protected Area (MMSPA) over long-time series Landsat images. concentrations chlorophyll-a (Chl-a), phosphate, dissolved inorganic nitrogen (DIN) sea area from 2002 2022 were inverted analyzed. spatial temporal characteristics these investigated. results indicated that random forest could reliably predict Chl-a, DIN MMSPA. Specifically, Chl-a showed coefficient determination (R2) 0.741, root mean square error (RMSE) 3.376 μg/L, absolute percentage (MAPE) 16.219%. Regarding distribution, parameters notably elevated nearshore zones, especially northwest, contrasted with lower offshore southeast areas. Predominantly, regions higher proximity aquaculture zones. Additionally, nutrients originating land sources, transported via rivers such as Yangtze River, well influenced by activities, have shaped nutrient distribution. Over long term, MMSPA has shown considerable interannual fluctuations during past two decades. As sanctuary, preserving superior healthy ecosystem very important. Efforts protection, restoration, management will demand labor. Remote demonstrated its worth proficient technology real-time monitoring, capable supporting sustainable exploitation resources safeguarding ecological environment.

Язык: Английский

Geostatistical and multivariate analysis of phosphate evolution and its relationship with heavy metals in shallow groundwater in a Semi-Arid Basin DOI
Saadu Umar Wali,

Noraliani Alias,

Abdulqadir Abubakar Usman

и другие.

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 18, 2025

Язык: Английский

Процитировано

2
Flood Susceptibility Assessment in Urban Areas via Deep Neural Network Approach DOI Open Access
Tatyana Panfilova, В В Кукарцев, В С Тынченко

и другие.

Sustainability, Год журнала: 2024, Номер 16(17), С. 7489 - 7489

Опубликована: Авг. 29, 2024

Floods, caused by intense rainfall or typhoons, overwhelming urban drainage systems, pose significant threats to areas, leading substantial economic losses and endangering human lives. This study proposes a methodology for flood assessment in areas using multiclass classification approach with Deep Neural Network (DNN) optimized through hyperparameter tuning genetic algorithms (GAs) leveraging remote sensing data of dataset the Ibadan metropolis, Nigeria Metro Manila, Philippines. The results show that DNN model significantly improves risk accuracy (Ibadan-0.98) compared datasets containing only location precipitation (Manila-0.38). By incorporating soil into model, as well reducing number classes, it is able predict risks more accurately, providing insights proactive mitigation strategies planning.

Язык: Английский

Процитировано

11
Assessing the impact of rainfall, topography, and human disturbances on nutrient levels using integrated machine learning and GAMs models in the Choctawhatchee River Watershed DOI
Shubo Fang, Matthew J. Deitch, Tesfay Gebretsadkan Gebremicael

и другие.

Journal of Environmental Management, Год журнала: 2025, Номер 375, С. 124361 - 124361

Опубликована: Янв. 31, 2025

Язык: Английский

Процитировано

0
Digital technologies for water use and management in agriculture: Recent applications and future outlook DOI Creative Commons
Carlos Parra-López, Saker Ben Abdallah, Guillermo Garcia‐Garcia

и другие.

Agricultural Water Management, Год журнала: 2025, Номер 309, С. 109347 - 109347

Опубликована: Фев. 2, 2025

Язык: Английский

Процитировано

0
Decoding drinking water flavor: A pioneering and interpretable machine learning approach DOI

Youwen Shuai,

Kejia Zhang, Tuqiao Zhang

и другие.

Journal of Water Process Engineering, Год журнала: 2025, Номер 72, С. 107577 - 107577

Опубликована: Март 30, 2025

Язык: Английский

Процитировано

0
Water quality evaluation in Liaoning Province large reservoirs: a new method integrating random forest-TOPSIS and Monte Carlo simulation DOI Creative Commons

Chong Zhang,

Mo Chen, Yi Wang

и другие.

Applied Water Science, Год журнала: 2025, Номер 15(5)

Опубликована: Апрель 7, 2025

Язык: Английский

Процитировано

0
Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach DOI
Md. Abdullah-Al Mamun, Abu Reza Md. Towfiqul Islam,

Mst Nazneen Aktar

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 953, С. 176024 - 176024

Опубликована: Сен. 4, 2024

Язык: Английский

Процитировано

1
Predicting Total Alkalinity in Saline Water Using Machine Learning: A Case Study with RapidMiner DOI
Thuan T. Nguyen, Quang Trung Le, Mary T. Doan

и другие.

Deleted Journal, Год журнала: 2024, Номер 4, С. 100032 - 100032

Опубликована: Ноя. 10, 2024

Язык: Английский

Процитировано

1
Water Quality in the Ma’an Archipelago Marine Special Protected Area: Remote Sensing Inversion Based on Machine Learning DOI Creative Commons
Zhixin Wang,

Zhenqi Zhang,

Hailong Li

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(10), С. 1742 - 1742

Опубликована: Окт. 3, 2024

Due to the increasing impact of climate change and human activities on marine ecosystems, there is an urgent need study water quality. The use remote sensing for quality inversion offers a precise, timely, comprehensive way evaluate present state future trajectories In this paper, model utilizing machine learning was developed variations in Ma’an Archipelago Marine Special Protected Area (MMSPA) over long-time series Landsat images. concentrations chlorophyll-a (Chl-a), phosphate, dissolved inorganic nitrogen (DIN) sea area from 2002 2022 were inverted analyzed. spatial temporal characteristics these investigated. results indicated that random forest could reliably predict Chl-a, DIN MMSPA. Specifically, Chl-a showed coefficient determination (R2) 0.741, root mean square error (RMSE) 3.376 μg/L, absolute percentage (MAPE) 16.219%. Regarding distribution, parameters notably elevated nearshore zones, especially northwest, contrasted with lower offshore southeast areas. Predominantly, regions higher proximity aquaculture zones. Additionally, nutrients originating land sources, transported via rivers such as Yangtze River, well influenced by activities, have shaped nutrient distribution. Over long term, MMSPA has shown considerable interannual fluctuations during past two decades. As sanctuary, preserving superior healthy ecosystem very important. Efforts protection, restoration, management will demand labor. Remote demonstrated its worth proficient technology real-time monitoring, capable supporting sustainable exploitation resources safeguarding ecological environment.

Язык: Английский

Процитировано

0