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

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(10), P. 1742 - 1742

Published: Oct. 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.

Language: Английский

Current Status of Emerging Contaminant Models and Their Applications Concerning the Aquatic Environment: A Review DOI Open Access
Zhuang Liu, Yonghai Gan, Jun Luo

et al.

Water, Journal Year: 2025, Volume and Issue: 17(1), P. 85 - 85

Published: Jan. 1, 2025

Increasing numbers of emerging contaminants (ECs) detected in water environments require a detailed understanding these chemicals’ fate, distribution, transport, and risk aquatic ecosystems. Modeling is useful approach for determining ECs’ characteristics their behaviors environments. This article proposes systematic taxonomy EC models addresses gaps the comprehensive analysis applications. The reviewed include conventional quality models, multimedia fugacity machine learning (ML) models. Conventional have higher prediction accuracy spatial resolution; nevertheless, they are limited functionality can only be used to predict contaminant concentrations Fugacity excellent at depicting how travel between different environmental media, but cannot directly analyze variations parts same media because model assumes that constant within compartment. Compared other ML applied more scenarios, such as identification assessments, rather than being confined concentrations. In recent years, with rapid development artificial intelligence, surpassed becoming one newest hotspots study ECs. primary challenge faced by outcomes difficult interpret understand, this influences practical value an some extent.

Language: Английский

Citations

3
Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications DOI Creative Commons
Maria Silvia Binetti, Carmine Massarelli, Vito Felice Uricchio

et al.

Machine Learning and Knowledge Extraction, Journal Year: 2024, Volume and Issue: 6(2), P. 1263 - 1280

Published: June 5, 2024

This is a systematic literature review of the application machine learning (ML) algorithms in geosciences, with focus on environmental monitoring applications. ML algorithms, their ability to analyze vast quantities data, decipher complex relationships, and predict future events, they offer promising capabilities implement technologies based more precise reliable data processing. considers several vulnerable particularly at-risk themes as landfills, mining activities, protection coastal dunes, illegal discharges into water bodies, pollution degradation soil matrices large industrial complexes. These case studies about provide an opportunity better examine impact human activities environment, specific matrices. The recent underscores increasing importance these contexts, highlighting preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional (CNNs), principal component analysis (PCA), much more. In field management, following methodologies invaluable insights that can steer strategic planning decision-making accurate image classification, prediction models, object detection recognition, map variable predictions.

Language: Английский

Citations

6
A multi-method approach to assess long-term urbanization impacts on an ecologically sensitive urban wetland in Northeast India DOI
Daisy Koch, Dhrubajyoti Sen, Venkatesh Uddameri

et al.

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 966, P. 178681 - 178681

Published: Feb. 1, 2025

Language: Английский

Citations

0
How Hydrological Extremes Affect the Chlorophyll-a Concentration in Inland Water in Jiujiang City, China: Evidence from Satellite Remote Sensing DOI Creative Commons
Wei Jiang, Xiaohui Ding,

Fanping Kong

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2025, Volume and Issue: 14(2), P. 85 - 85

Published: Feb. 15, 2025

From 2020 to 2022, hydrological extremes such as severe floods and droughts occurred successively in Jiujiang city, Poyang Lake Basin, posing a threat regional water quality safety. The chlorophyll-a (Chl-a) concentration is key indicator of river eutrophication. Until now, there has been lack empirical research exploring the Chl-a trend inland context extremes. In this study, Sentinel-2 satellite remote sensing data sourced from Google Earth Engine (GEE) cloud platform, along with hourly collected monitoring stations Jiangxi Province, China, are utilized develop quantitative inversion model for concentration. concentrations various types were estimated each quarter spatiotemporal distribution was analyzed. main findings follows: (1) validated via situ measurements, coefficient determination 0.563; (2) spatial estimates revealed slight increasing trend, by 0.1193 μg/L closely aligning monitoring-station data; (3) an extreme drought 2022 led less bodies, consequently, displayed significant upward especially Lake, where mean increased approximately 1 Q1 Q2 2022. These seasonal changes waters events, thus providing valuable information sustainable management city.

Language: Английский

Citations

0
Comparing the performance of 10 machine learning models in predicting Chlorophyll a in western Lake Erie DOI
Yang Song, Chunqi Shen, Hong Yi

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 125007 - 125007

Published: March 17, 2025

Language: Английский

Citations

0
Integrated ensemble learning approach for multi-depth water quality estimation in reservoir environments DOI
Mohammad Sadegh Zare, Mohammad Reza Nikoo, Ghazi Al-Rawas

et al.

Journal of Water Process Engineering, Journal Year: 2024, Volume and Issue: 66, P. 105840 - 105840

Published: Aug. 12, 2024

Language: Английский

Citations

3
Water quality parameters retrieval and nutrient status evaluation based on machine learning methods and Sentinel- 2 imagery: a case study of the Hongjiannao Lake DOI
Ying Liu, Zhixiong Wang, Hui Yue

et al.

Environmental Monitoring and Assessment, Journal Year: 2025, Volume and Issue: 197(5)

Published: April 15, 2025

Language: Английский

Citations

0
Long-Term AI Prediction of Ammonium Levels in River Lee in London Using Transformer and Ensemble Models DOI Creative Commons
Ali J. Ali, Ashraf Ahmed

Cleaner Water, Journal Year: 2024, Volume and Issue: unknown, P. 100051 - 100051

Published: Oct. 1, 2024

Language: Английский

Citations

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

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(10), P. 1742 - 1742

Published: Oct. 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.

Language: Английский

Citations

0