Performances of MLR, RBF-NN, and MLP-NN in the evaluation and prediction of water resources quality for irrigation purposes under two modeling scenarios DOI
Johnbosco C. Egbueri, Johnson C. Agbasi

Geocarto International, Journal Year: 2022, Volume and Issue: 37(26), P. 14399 - 14431

Published: June 9, 2022

One of the pivotal decision-making tools for sustainable management water resources various uses is accurate prediction quality. In present paper, multiple linear regression (MLR), radial basis function neural network (RBF-NN), and multilayer perceptron (MLP-NN) models were developed monitoring irrigation quality (IWQ) in Ojoto area, southeastern Nigeria. This paper first to integrate simultaneously implement these predictive methods modeling seven IWQ indices. Moreover, two scenarios considered. Scenario 1 represents predictions that utilized specific physicochemical parameters calculating indices as input variables while 2 pH, EC, Na+, K+, Mg2+, Ca2+, Cl-, SO42-, HCO3- inputs. terms salinity hazard, most are unsuitable/poor irrigation. However, carbonate bicarbonate impact magnesium majority samples have good excellent IWQ. Seven agglomerative Q-mode dendrograms spatiotemporally classified based on Model validation metrics showed MLR, RBF-NN, MLP-NN performed well both scenarios, with minor variations.

Language: Английский

Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model DOI
Usman Mohseni,

Chaitanya B. Pande,

Subodh Chandra Pal

et al.

Chemosphere, Journal Year: 2024, Volume and Issue: 352, P. 141393 - 141393

Published: Feb. 5, 2024

Language: Английский

Citations

37
Assessing water quality of an ecologically critical urban canal incorporating machine learning approaches DOI Creative Commons
Abdul Majed Sajib, Mir Talas Mahammad Diganta, Md Moniruzzaman

et al.

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

Citations

35
Advances in machine learning and IoT for water quality monitoring: A comprehensive review DOI Creative Commons
Ismail Essamlali, Hasna Nhaila, Mohamed El Khaïli

et al.

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

Citations

32
Reliable water quality prediction and parametric analysis using explainable AI models DOI Creative Commons
M. K. Nallakaruppan,

E. Gangadevi,

M. Lawanya Shri

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: March 29, 2024

Abstract The consumption of water constitutes the physical health most living species and hence management its purity quality is extremely essential as contaminated has to potential create adverse environmental consequences. This creates dire necessity measure, control monitor water. primary contaminant present in Total Dissolved Solids (TDS), which hard filter out. There are various substances apart from mere solids such potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic other pollutants. proposed work aims provide automation estimation through Artificial Intelligence uses Explainable (XAI) for explanation significant parameters contributing towards potability impurities. XAI transparency justifiability a white-box model since Machine Learning (ML) black-box unable describe reasoning behind ML classification. models Logistic Regression, Support Vector (SVM), Gaussian Naive Bayes, Decision Tree (DT) Random Forest (RF) classify whether drinkable. representations force plot, test patch, summary dependency plot decision generated SHAPELY explainer explain features, prediction score, feature importance justification estimation. RF classifier selected yields optimum Accuracy F1-Score 0.9999, with Precision Re-call 0.9997 0.998 respectively. Thus, an exploratory analysis indicators associated their significance. emerging research at vision addressing future well.

Language: Английский

Citations

23
An integrated appraisal of the hydrogeochemistry and the potential public health risks of groundwater nitrate and fluoride in eastern Ghana DOI
Johnbosco C. Egbueri, Mahamuda Abu, Johnson C. Agbasi

et al.

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

Published: June 28, 2024

Language: Английский

Citations

23
Towards sustainable industrial development: modelling the quality, scaling potential and corrosivity of groundwater using GIS, spatial statistics, soft computing and index-based methods DOI
Johnson C. Agbasi, Mahamuda Abu, Johnbosco C. Egbueri

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: June 21, 2024

Language: Английский

Citations

18
Groundwater quality assessment for drinking and irrigation uses within the vicinities of Volta Lake and Akosombo Dam in Ghana: a multi-methodological approach DOI
Mahamuda Abu, Johnbosco C. Egbueri, Johnson C. Agbasi

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(7)

Published: March 26, 2025

Language: Английский

Citations

2
Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India DOI
Ahmed Elbeltagi,

Chaitanya B. Pande,

Saber Kouadri

et al.

Environmental Science and Pollution Research, Journal Year: 2021, Volume and Issue: 29(12), P. 17591 - 17605

Published: Oct. 20, 2021

Language: Английский

Citations

80
Prediction of groundwater quality indices using machine learning algorithms DOI Creative Commons
Hemant Raheja, Arun Goel, Mahesh Pal

et al.

Water Practice & Technology, Journal Year: 2021, Volume and Issue: 17(1), P. 336 - 351

Published: Dec. 1, 2021

Abstract The present paper deals with performance evaluation of application three machine learning algorithms such as Deep neural network (DNN), Gradient boosting (GBM) and Extreme gradient (XGBoost) to evaluate the ground water indices over a study area Haryana state (India). To investigate applicability these models, two quality indices, namely Entropy Water Quality Index (EWQI) (WQI) are employed in study. Analysis results demonstrated that DNN has exhibited comparatively lower error values it performed better prediction both i.e. EWQI WQI. Correlation Coefficient (CC = 0.989), Root Mean Square Error (RMSE 0.037), Nash–Sutcliffe efficiency (NSE 0.995), agreement (d 0.999) for CC 0.975, RMSE 0.055, NSE 0.991, d 0.998 WQI have been obtained. From variable importance input parameters, Electrical conductivity (EC) was observed be most significant ‘pH’ least parameter predictions using models. It is envisaged can used righteously predict groundwater decide its potability.

Language: Английский

Citations

75
Specific heat capacity of molten salt-based nanofluids in solar thermal applications: A paradigm of two modern ensemble machine learning methods DOI
Mehdi Jamei, Masoud Karbasi, Ismail Adewale Olumegbon

et al.

Journal of Molecular Liquids, Journal Year: 2021, Volume and Issue: 335, P. 116434 - 116434

Published: May 14, 2021

Language: Английский

Citations

61