Estimation and characterization of physical and inorganic chemical indicators of water quality by using SAR images

Shareef, Muntadher Aidi; Toumi, Abdelmalek; Khenchaf, Ali

doi:10.1117/12.2194503

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…However, Ref. [114] used a correlation factor of 0.64 in their study to estimate and characterize physical and organic chemical indicators of water quality. Both TDS and EC are used to describe the salinity level in the water.…”

Section: Measurement Of Tdsmentioning

confidence: 99%

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

et al. 2023

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This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

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Section: Measurement Of Tdsmentioning

confidence: 99%

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

et al. 2023

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“…Thermal infrared data have been used to retrieve water temperature [45][46][47]. Radar data have been used to retrieve EC [48,49], salinity [48,49], and total dissolved salts [48,49]. Previous efforts that used remote sensing products to monitor WQIs over multiple ecosystems commonly used empirical models [7,19,20,26,27,29,50].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

Bygate,

Ahmed

2024

Remote Sensing

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Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems.

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“…An empirical factor of 0.67 is adopted for natural water systems according to a study by [28]; this, however, is slightly different from the 0.64 value used by [29] in their study on water quality physical and organic chemical indicators estimation and characterization.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

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Estimation and characterization of physical and inorganic chemical indicators of water quality by using SAR images

Cited by 3 publications

References 16 publications

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

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