Robust machine learning algorithms for predicting coastal water quality index

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 321, P. 115923 - 115923

Published: Aug. 19, 2022

Coastal water quality assessment is an essential task to keep "good quality" status for living organisms in coastal ecosystems. The Water index (WQI) a widely used tool assess but this technique has received much criticism due the model's reliability and inconsistence. present study recently developed improved WQI model calculating WQIs Cork Harbour. aim of research determine most reliable robust machine learning (ML) algorithm(s) anticipate at each monitoring point instead repeatedly employing SI weight values order reduce uncertainty. In study, we compared eight commonly algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra (ExT), Support Vector Machine (SVM), Linear Regression (LR), Gaussian Naïve Bayes (GNB). For purposes developing prediction models, dataset was divided into two groups: training (70%) testing (30%), whereas models were validated using 10-fold cross-validation method. evaluate models' performance, RMSE, MSE, MAE, R2, PREI metrics study. tree-based DT (RMSE = 0.0, MSE MAE R2 1.0 PERI 0.0) ExT ensemble XGB +0.16 -0.17) RF 2.0, 3.80, 1.10, 0.98, +3.52 -25.38) outperformed other models. results performance indicate that DT, ExT, GXB could be effective, significantly uncertainty predicting WQIs. findings are also useful reducing optimizing WQM-WQI architecture values.

Language: Английский

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A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction

Ecological Indicators, Journal Year: 2023, Volume and Issue: 146, P. 109882 - 109882

Published: Jan. 9, 2023

With the accelerated industrialization and urbanization process, water pollution in rivers is being increasingly worsened, has caused a series of ecological environmental issues. The prediction river quality index (WQI) prerequisite for prevention management. However, data non-smooth non-linear, strong coupling relationship between different parameters that influence each other observed, making it an inevitable problem to accurately predict parameters. To this end, combination machine learning intelligent optimization algorithms was hereby used break dilemma. Specifically, Back Propagation Neural Network (BPNN) model established using Artificial Bee Colony (ABC) algorithm, with three adaptive evolutionary strategies, i.e., dynamic factors, probability selection gradient initialization combined form Adaptive Evolutionary (AEABC) algorithm. experimental results algorithm demonstrate AEABC-BPNN only requires 14 iterations converge case. predictions WQI can reduce error evaluation indicators mean square (MSE) 0.2745, which at least 25.2% lower than those rest compared, absolute percentage (MAPE) 7.58%. In four WQIs, interval coverage (PICP) reaches 100%. Besides, robustness testing experiments were also designed verify still outperforms terms accuracy when guided by historical data. proposed plays pivotal role management lakes, scientific significance future protection.

Language: Английский

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Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling

Sustainability, Journal Year: 2023, Volume and Issue: 15(9), P. 7593 - 7593

Published: May 5, 2023

The present study was carried out using artificial neural network (ANN) model for predicting the sodium hazardness, i.e., adsorption ratio (SAR), percent (%Na) residual, Kelly’s (KR), and residual carbonate (RSC) in groundwater of Pratapgarh district Southern Rajasthan, India. This focuses on verifying suitability water irrigational purpose, wherein more decline coupled with quality problems compared to other areas are observed. southern part Rajasthan State is populated as rest parts. which leads industrialization, urbanization, evolutionary changes agricultural production region. Therefore, it necessary propose innovative methods analyzing (WQ) use. aims develop an optimized predict hazardness irrigation purposes. ANN developed ‘nntool’ MATLAB software. trained validated ten years (2010–2020) data. An L-M 3-layer back propagation technique adopted architecture a reliable accurate irrigation. Furthermore, statistical performance indicators, such RMSE, IA, R, MBE, were used check consistency prediction results. model, ANN4 (3-12-1), (4-15-1), ANN1 (4-5-1), found best suited SAR, %Na, RSC, KR indicators district. analysis (3-12-1) led correlation coefficient = 1, IA RMS 0.14, MBE 0.0050. Hence, proposed provides satisfactory match empirically generated datasets observed wells. development modeling may help useful planning sustainable management resources crop plans per quality.

Language: Английский

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Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

Water, Journal Year: 2022, Volume and Issue: 14(10), P. 1552 - 1552

Published: May 12, 2022

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level existing surface water. This case study aims evaluate performance twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient histogram-based light extreme boosting), three decision tree-based (decision tree, extra trees, random forest), four ANN-based (multilayer perceptron, radial basis function, deep feed-forward neural network, convolutional network), in estimating quality La Buong River Vietnam. Water data at monitoring stations alongside for period 2010–2017 were utilized calculate index (WQI). Prediction ML models was evaluated by using two efficiency statistics (i.e., R2 RMSE). The results indicated that all have good predicting WQI but boosting (XGBoost) has best with highest accuracy (R2 = 0.989 RMSE 0.107). findings strengthen argument especially XGBoost, may be employed prediction a high accuracy, which will further improve management.

Language: Английский

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Assessment of PTEs in water resources by integrating HHRISK code, water quality indices, multivariate statistics, and ANNs

Geocarto International, Journal Year: 2022, Volume and Issue: 37(25), P. 10407 - 10433

Published: Jan. 26, 2022

The use of contaminated water for drinking and sanitary purposes can be detrimental to human health. In this article, the Human Health Risk (HHRISK) code was applied, alongside modified heavy metal index (MHMI), synthetic pollution (SPI), entropy-weighted quality (EWQI), investigate status, ingestion, dermal health risks potentially toxic elements (PTEs) (Fe, Zn, Mn, Pb, Cr, Ni) in resources from Umunya area, Nigeria. Physicochemical measurements followed standard methods. Results MHMI, SPI, EWQI revealed that about 60% samples had low were considered suitable consumption, while 40% unsuitable. Further, cumulative non-carcinogenic risk scores indicated pose low-medium high child adult populations. Contrarily, results carcinogenic showed 6.67% expose users risks, whereas 93.33% them risks. Although there are agreements between both populations (regarding risks), it is worth highlighting children higher. Therefore, study area more vulnerable Also, due ingestion higher than contact. Linear regression analysis strong agreement indexical models While artificial neural networks multiple linear accurately predicted indices, hierarchical dendrograms efficiently classed into various spatiotemporal groups.

Language: Английский

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Influential paths of ecosystem services on human well-being in the context of the sustainable development goals

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 852, P. 158443 - 158443

Published: Aug. 31, 2022

Language: Английский

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Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 406, P. 136885 - 136885

Published: April 3, 2023

Language: Английский

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Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches

Ecological Informatics, Journal Year: 2024, Volume and Issue: 80, P. 102514 - 102514

Published: Feb. 13, 2024

This study assessed water quality (WQ) in Tongi Canal, an ecologically critical and economically important urban canal Bangladesh. The researchers employed the Root Mean Square Water Quality Index (RMS-WQI) model, utilizing seven WQ indicators, including temperature, dissolve oxygen, electrical conductivity, lead, cadmium, iron to calculate index (WQI) score. results showed that most of sampling locations poor WQ, with many indicators violating Bangladesh's environmental conservation regulations. eight machine learning algorithms, where Gaussian process regression (GPR) model demonstrated superior performance (training RMSE = 1.77, testing 0.0006) predicting WQI scores. To validate GPR model's performance, several measures, coefficient determination (R2), Nash-Sutcliffe efficiency (NSE), factor (MEF), Z statistics, Taylor diagram analysis, were employed. exhibited higher sensitivity (R2 1.0) (NSE 1.0, MEF 0.0) WQ. analysis uncertainty (standard 7.08 ± 0.9025; expanded 1.846) indicates RMS-WQI holds potential for assessing inland waterbodies. These findings indicate could be effective approach waters across study's did not meet recommended guidelines, indicating Canal is unsafe unsuitable various purposes. implications extend beyond contribute management initiatives

Language: Английский

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Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Heliyon, Journal Year: 2024, Volume and Issue: 10(6), P. e27920 - e27920

Published: March 1, 2024

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality ensure usability. The advent of the. Internet Things (IoT) has brought about revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring water (WQ). By employing Machine learning (ML) techniques, this gathered can be analyzed make accurate predictions regarding quality. These predictive insights play crucial role decision-making processes aimed at safeguarding quality, such identifying areas need immediate attention and implementing preventive measures avert contamination. This paper aims provide comprehensive review current state art monitoring, with specific focus on employment IoT wireless technologies ML techniques. study examines utilization range technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, Bluetooth, context Furthermore, it explores application both supervised unsupervised algorithms for analyzing interpreting collected data. In addition discussing art, survey also addresses challenges open research questions involved integrating (WQM).

Language: Английский

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An integrated appraisal of the hydrogeochemistry and the potential public health risks of groundwater nitrate and fluoride in eastern Ghana

Groundwater for Sustainable Development, Journal Year: 2024, Volume and Issue: 26, P. 101264 - 101264

Published: June 28, 2024

Language: Английский

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