Monitoring of Total Dissolved Solids Using Remote Sensing Band Reflectance and Salinity Indices: A Case Study of the Imperial County Section, AZ-CA, of the Colorado River

Adjovu, Godson Ebenezer; Stephen, Haroon; Ahmad, Sajjad

doi:10.1061/9780784484258.106

Cited by 10 publications

(23 citation statements)

References 43 publications

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“…Lake Mead is the study area for this study. The lake lies on the Colorado River and it is the largest reservoir on the approximately 2333 km river located in the Mohave Desert, Arizona-Nevada region [1,2]. Lake Mead was formed after Hoover Dam was constructed in the 1930s and it is the largest reservoir in terms of water capacity within the United States.…”

Section: Study Area Descriptionsmentioning

confidence: 99%

“…Lake Mead, which lies on the Colorado River, is the largest reservoir in the Mohave Desert, Arizona-Nevada region in the Colorado River Basin (CRB) [1][2][3]. The lake is a crucial source of water for more than 25 million people in the southwestern US.…”

Section: Introductionmentioning

confidence: 99%

“…Some of these anthropogenic are unintended consequences that need to be minimized [8]. TDS-related economic damages to the CRB are estimated to be over USD 300 million per year, destroying millions of acres of irrigated lands [2,3,[9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…TDS analysis is necessary and key as it offers a better understanding of the quality of water, particularly the groundwater, and the effect of seawater intrusion compared with EC analysis. Field measurement of WQPs such as TDS is, however, more difficult, cost-intensive, and time-consuming [14,[30][31][32] as compared with EC measurement, as more equipment and time are required for its measurement [27]. EC measurement is quite easy and economical and can be carried out in situ using portable water quality checker devices [27].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Study Area Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Monitoring, management, and forecasting of the entire water bodies may be inaccessible due to the topographic terrain of the water body. Furthermore, the accuracy and precision of sampled in situ data can be questionable owing to both errors that may arise from field sampling and laboratory analysis [6][7][8][9]. These limitations have put constraints on efforts to monitor the ecological suitability of water bodies over the past decades.…”

Section: Introductionmentioning

confidence: 99%

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

et al. 2023

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This study provides an overview of the techniques, shortcomings, and strengths of remote sensing (RS) applications in the effective retrieval and monitoring of water quality parameters (WQPs) such as chlorophyll-a concentration, turbidity, total suspended solids, colored dissolved organic matter, total dissolved solids among others. To be effectively retrieved by RS, these WQPs are categorized as optically active or inactive based on their influence on the optical characteristics measured by RS sensors. RS applications offer the opportunity for decisionmakers to quantify and monitor WQPs on a spatiotemporal scale effectively. The use of RS for water quality monitoring has been explored in many studies using empirical, analytical, semi-empirical, and machine-learning algorithms. RS spectral signatures have been applied for the estimation of WQPs using two categories of RS, namely, microwave and optical sensors. Optical RS, which has been heavily applied in the estimation of WQPs, is further grouped as spaceborne and airborne sensors based on the platform they are on board. The choice of a particular sensor to be used in any RS application depends on various factors including cost, and spatial, spectral, and temporal resolutions of the images. Some of the known satellite sensors used in the literature and reviewed in this paper include the Multispectral Instrument aboard Sentinel-2A/B, Moderate Resolution Imaging Spectroradiometer, Landsat Thematic Mapper, Enhanced Thematic Mapper, and Operational Land Imager.

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Utilization of Machine Learning Models and Satellite Data for the Estimation of Total Dissolved Solids in the Colorado River System

Adjovu

Shaikh

Stephen

et al. 2023

World Environmental and Water Resources Congress 2023

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Monitoring of Total Dissolved Solids Using Remote Sensing Band Reflectance and Salinity Indices: A Case Study of the Imperial County Section, AZ-CA, of the Colorado River

Cited by 10 publications

References 43 publications

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

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

Overview of the Application of Remote Sensing in Effective Monitoring of Water Quality Parameters

Utilization of Machine Learning Models and Satellite Data for the Estimation of Total Dissolved Solids in the Colorado River System

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