Assessment of water quality parameters in Muthupet estuary using hyperspectral PRISMA satellite and multispectral images

Rahul, T. S.; Brema, J.

doi:10.1007/s10661-023-11497-y

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…A paradigm shift in recent decades has led to extensive satellite datasets being made freely available to the research community, and with the advent of newer satellites with increased spatial resolution (e.g., 10 m or 20 m for most bands of Sentinel 2) and more appropriate spectral bands, an increased amount of research has focused on smaller inland waters such as large rivers and medium to large-scale lakes and reservoirs [1]. Several studies, taking advantage of such datasets and in some cases, of machine learning approaches, have focused their attention on monitoring/predicting optically active constituents such as chlorophyll-a (chl-a) [2][3][4][5][6], total suspended/dissolved solids [3,7,8] and coloured dissolved organic matter (cDOM) [9], and through indirect approaches, other parameters such as nutrients [10,11], or dissolved carbon dioxide [12] and other carbon fractions [7,13]. Reviews are available [1] with comprehensive breakdowns of target application and modelling approach.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Prediction of Satellite-Derived Water Quality for a Drinking Water Reservoir

Bertone

Hughes

2023

Sustainability

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A Bayesian network-based modelling framework was proposed to predict the probability of exceeding critical thresholds for chlorophyll-a and turbidity in an Australian subtropical drinking water reservoir, based on Sentinel-2 data and prior knowledge. The model was trained with quasi-synchronous historical in situ and satellite data for 2018–2023 and achieved satisfactory accuracy (Brier score < 0.27 for all models) despite limited poor water quality events in the final dataset. The graphical output of the model (posterior probability maps of high turbidity or chlorophyll-a) provides an effective means for the user to evaluate both the prediction, and the uncertainty behind the predictions in a single map. This avoids loss of trust in the model and can trigger spatially targeted data collection in order to reduce uncertainty. Future work will focus on refining the modelling methodology and its automation, as well as including other data such as in situ high-frequency sensors.

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Section: Introductionmentioning

confidence: 99%

Probabilistic Prediction of Satellite-Derived Water Quality for a Drinking Water Reservoir

Bertone

Hughes

2023

Sustainability

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“…Scholars are continuously looking for solutions for effective drinking water quality assessment. Traditional methods of measuring and estimating drinking water quality usually involve a combination of physical, chemical, and microbiological tests, for example, turbidity tests [13,14], total dissolved solids tests, and total suspended matter tests [15][16][17], in addition to pH measurement [18,19]; these methods are especially employed in areas with limited technological infrastructure, and lacking advanced equipment and methodologies which would offer more comprehensive and accurate analyses of drinking water quality. The scientific literature evidences effective nanotechnology methods used in the measurement of major cations, anions, and heavy metals in water [20,21].…”

Section: Introductionmentioning

confidence: 99%

Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

Gorelova

Bellini

et al. 2023

IJERPH

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Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.

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Research progress in surface water quality monitoring based on remote sensing technology

Zheng,

Wang,

Kondratenko

et al. 2024

International Journal of Remote Sensing

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Assessment of water quality parameters in Muthupet estuary using hyperspectral PRISMA satellite and multispectral images

Cited by 3 publications

References 52 publications

Probabilistic Prediction of Satellite-Derived Water Quality for a Drinking Water Reservoir

Probabilistic Prediction of Satellite-Derived Water Quality for a Drinking Water Reservoir

Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

Research progress in surface water quality monitoring based on remote sensing technology

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