Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models

Adusei, Yvonne Yeboah; Quaye‐Ballard, Jonathan Arthur; Adjaottor, Albert Amatey; Mensah, Alex Appiah

doi:10.1016/j.ejrs.2021.06.006

Cited by 12 publications

(11 citation statements)

References 25 publications

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“…This sets them apart from opaque algorithms like NNs and support vector machines (SVMs). SVM algorithms hold a prominent position in the field of satellite-based water quality monitoring, and their inclusion in numerous studies showcases their remarkable potential for this application [22,25,26,30,33,34,40,44,46,75,178,181,182,185,192,194,[200][201][202]. SVMs, in particular, prove to be highly suitable for satellite-based water quality monitoring due to their exceptional capability to handle large datasets characterized by a high number of features.…”

Section: Machine or Deep Learning Model Choicementioning

confidence: 99%

“…Therefore, the accuracy of atmospheric correction methods directly impacts the quality of input data for modeling purposes. Sensors with fine spatial resolution, like OLI [22,23,25,[29][30][31]41,44,45,51,65,139,161,165,174,178,181,182,188,194,203,[205][206][207]209,215,217,219,246,268] and MSI [21,22,29,33,34,65,161,162,164,165,170,171,182,187,193,204,208,211,213,2...…”

Section: Satellite Image Data Quality and Sensor Choicementioning

confidence: 99%

“…These compounds are referred to as non-optically-active compounds (nOACs). To address this, researchers have focused on modeling nOACs, such as phosphorous pollutants (PP) [39,51,158,170,173,178,207], nitrogen pollutants (NP) [170,172,178,207,225], COD [172,205], pH [175,184], and DO [22,29,65,182,205,218], using machine learning models based on satellite data. In our study, we compared the median R 2 values of optically active compounds (OACs), such as Chl-a, turbidity, SS, and SDD, with those of nOACs, including DO, PP, NP, BOD [205], and COD.…”

Section: Water Quality Parametersmentioning

confidence: 99%

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Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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This review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution.

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Section: Machine or Deep Learning Model Choicementioning

confidence: 99%

Section: Satellite Image Data Quality and Sensor Choicementioning

confidence: 99%

Section: Water Quality Parametersmentioning

confidence: 99%

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Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Mukonza,

Chiang

2023

Environments

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“…According to the pie chart percentile, it is obvious that the bulk of the geospatial research was conducted on lakes [27] [28] [29] [29] [30], and inland water bodies [31], [32] [15] [33] with no distinction made between the two types of water bodies. Again, according to this review study, 14 percent of the research articles were focused on the bay [19] [33] [34] , sea [14], [35] [36], and coastal environment [31] [11] [20], whereas marsh (10%) [13][12], reservoir (5%) [37], and estuaries (5%) [19] were found to be at the bottom of the priority list. Finally, researchers found that other parameters were employed in 24 percent of study publications that were ascribed less relevance by the researchers.…”

Section: Water Bodiesmentioning

confidence: 99%

“…Finally, researchers found that other parameters were employed in 24 percent of study publications that were ascribed less relevance by the researchers. Those are related to one's water-leaving reflectance (pw) Remote sensing reflectance (Rrs) [15], chlorophytes [19], total suspended matter (TSM) [38], total suspended solids, thermal pollution [28], pH, alkalinity, total dissolved solids and dissolved oxygen [37], Suspended sediment (SS), Secchi disk depth (SDD) [12], Water salinity and SO4 and CaCO3 levels [13], Cyanobacterial-dominance, surface scums and floating vegetation [31]. and Sentinel-3 [35].…”

Section: Water Bodiesmentioning

confidence: 99%

State-of-the-Art Review of Assessing Water Quality from Space

prapti

Shahar²,

Shariff

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Water acts as the medium which helps supply seafood and freshwater food for human consumption and drinking water. It is thus imperative that such a precious resource should be well monitored to safeguard human health and survival. Conventionally water quality can be monitored through manual laboratory-based methods which are time-consuming. Ground-based sensors are helping in expediting this task, though it involves the use of multiple sensors at various locations and requires regular maintenance and replacement. Satellite technology provides a good alternative method as it can scan large areas at a relatively low cost. Measurements of parameters such as sea surface temperature, total suspended solids (turbidity), primary food production (chlorophyll A), abnormal movement of aquatic animal, disease occurrence, water oxygen deficiency, growth monitoring of aquatic life and many others have been successfully reported in the literature. With the advent of micro-satellites that can transmit higher resolution images with a finer spectral resolution, greater advancements can be made for the detection of a greater number of critical water quality parameters. This paper reviewed the existing status of the water quality monitoring from space technology and covered published research done in various parts of the globe, including the problems, solutions, algorithm used, the advantages of the study, research region, water bodies, water quality variable, and satellites data.

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Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

Abbas

Alameddine

2023

Environ Monit Assess

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Spatial prediction and mapping of water quality of Owabi reservoir from satellite imageries and machine learning models

Cited by 12 publications

References 25 publications

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring

State-of-the-Art Review of Assessing Water Quality from Space

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

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