An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Sun, Kangjie; Rajabtabar, Mohammad; Phillips, R. C.; Rezaie-Balf, Mohammad; Ghaemi, Alireza; Band, Shahab S.; Mosavi, Amir

doi:10.1080/19942060.2020.1861987

Cited by 20 publications

(12 citation statements)

References 61 publications

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“…After the model is developed, models then need to be calibrated and validated. Developed models are trained and calibrated to ensure accuracy in the prediction of WQPs and evaluated using statistical metrics such as Nash-Sutcliffe Efficiency (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percent Mean Absolute Relative Error (PMARE) [204][205][206][207][208]. The evaluated models are then used to predict and map of WQPs: WQPs are predicted using the developed models and then mapped to assess the spatiotemporal extent of water degradation or pollution.…”

Section: Remote Sensing Applications In Water Quality Monitoringmentioning

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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Section: Remote Sensing Applications In Water Quality Monitoringmentioning

confidence: 99%

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

et al. 2023

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“…Additionally, we utilized the scatter index (SI), which is a representation of RMSE difference to the mean of the observed values, the Nash-Sutcliffe model efficiency (NSE) coefficient, which is a measure of the relative magnitude of the residual variance to the measured data, and percent bias (PBIAS), which is the probability of the predicted outcome to be greater or less than the observed value [45,97,98,100,101]. Lower values of RMSE, MAE, PMARE, and SI are an indication of better performance of the model, with zero indicating a perfect score [32,66,69,97,98,[100][101][102]. NSE values range from −∞ to 1, with values of about 0.75 to 1.00 said to be very good and values less than 0.4 being unsatisfactory.…”

Section: Model Evaluation Metricsmentioning

confidence: 99%

“…Positive and negative PBIAS represents underestimation and overestimation in the precision, respectively [97,98,103]. Equations for the model evaluation metrics are presented in Equations ( 2)-( 8) [32,66,69,97,98,[100][101][102].…”

Section: Model Evaluation Metricsmentioning

confidence: 99%

“…The models are ranked for each dataset according to how well they perform in various measures with the highest R 2 and NSE assigned 1 and the lowest assigned 9. Similarly, models with lower values of MAE, RMSE, PMARE, SI, and absolute values of PBIAS are assigned 1, and the models with the highest values of these metrics are assigned 9 [32,66,69,97,98,[100][101][102].…”

Section: Model Performance Assessmentmentioning

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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“…Pollutants, herbicides, and hydrocarbons are among the organic ions. TDS also includes organic compounds from the soil, such as humic and fulvic acids [31]. Total dissolved solids (TDS) are a measure of water purity and suitability for drinking and irrigation.…”

Section: Total Dissolved Solids (Tds)mentioning

confidence: 99%

Assessment of Physico-Chemical Properties of Drinking Water in District Mardan, Khyber Pakhtunkhwa, Pakistan

Shah

Islam

et al. 2022

J. Trop. Pharm. Chem.

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The issue of water quality is becoming ever more serious as freshwater resources are severely degraded across the world. Water quality in rivers, streams, lakes, and subterranean aquifers is deteriorating, posing a threat to human life and ecological sustainability. A proper study of the physico-chemical properties of the water samples give us insight into the quality of life in a specific region, as poor water quality not only affects the aquatic life but the surrounding ecosystem as well. In the present study, 30 different water samples were collected from different regions of district Mardan and they were analyzed for their physico-chemical properties such as pH, total suspended solids, conductivity, total dissolved solids, nitrates concentration, sulphates concentration, chlorides concentration, dissolved oxygen, and biochemical oxygen demand. The results of the analysis show that most of the parameters have significantly higher values than the WHO permissible limits. From the analysis of water samples, the researchers have been able to designate the study area with poor water quality and to propose future advice for sustaining the water quality in the region.

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An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Cited by 20 publications

References 61 publications

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

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

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

Assessment of Physico-Chemical Properties of Drinking Water in District Mardan, Khyber Pakhtunkhwa, Pakistan

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