scholarly journals Groundwater Quality Monitoring Using In-Situ Measurements and Hybrid Machine Learning with Empirical Bayesian Kriging Interpolation Method

2021 ◽  
Vol 12 (1) ◽  
pp. 132
Author(s):  
Delia B. Senoro ◽  
Kevin Lawrence M. de Jesus ◽  
Leonel C. Mendoza ◽  
Enya Marie D. Apostol ◽  
Katherine S. Escalona ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heavy Metal ◽  
Groundwater Quality ◽  
Quality Monitoring ◽  
Hybrid Technique ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Groundwater Quality Monitoring ◽  
Hybrid Machine

This article discusses the assessment of groundwater quality using a hybrid technique that would aid in the convenience of groundwater (GW) quality monitoring. Twenty eight (28) GW samples representing 62 barangays in Calapan City, Oriental Mindoro, Philippines were analyzed for their physicochemical characteristics and heavy metal (HM) concentrations. The 28 GW samples were collected at suburban sites identified by the coordinates produced by Global Positioning System Montana 680. The analysis of heavy metal concentrations was conducted onsite using portable handheld X-Ray Fluorescence (pXRF) Spectrometry. Hybrid machine learning—geostatistical interpolation (MLGI) method, specific to neural network particle swarm optimization with Empirical Bayesian Kriging (NN-PSO+EBK), was employed for data integration, GW quality spatial assessment and monitoring. Spatial map of metals concentration was produced using the NN-PSO-EBK. Another, spot map was created for observed metals concentration and was compared to the spatial maps. Results showed that the created maps recorded significant results based on its MSEs with values such as 1.404 × 10−4, 5.42 × 10−5, 6.26 × 10−4, 3.7 × 10−6, 4.141 × 10−4 for Ba, Cu, Fe, Mn, Zn, respectively. Also, cross-validation of the observed and predicted values resulted to R values range within 0.934–0.994 which means almost accurate. Based on these results, it can be stated that the technique is efficient for groundwater quality monitoring. Utilization of this technique could be useful in regular and efficient GW quality monitoring.

scholarly journals Using Supervised Machine Learning and Empirical Bayesian Kriging to reveal Correlates and Patterns of COVID-19 Disease outbreak in sub-Saharan Africa: Exploratory Data Analysis

2020 ◽  
Author(s):  
Amobi Andrew Onovo ◽  
Akinyemi Atobatele ◽  
Abiye Kalaiwo ◽  
Christopher Obanubi ◽  
Ezekiel James ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Impact ◽  
Disease Outbreak ◽  
Rate Of Change ◽  
Sub Saharan Africa ◽  
Supervised Machine Learning ◽  
Empirical Bayesian ◽  
Bayesian Kriging ◽  
Empirical Bayesian Kriging ◽  
Sub Saharan

AbstractIntroductionCoronavirus disease 2019 (COVID-19) is an emerging infectious disease that was first reported in Wuhan1,2, China, and has subsequently spread worldwide. Knowledge of coronavirus-related risk factors can help countries build more systematic and successful responses to COVID-19 disease outbreak. Here we used Supervised Machine Learning and Empirical Bayesian Kriging (EBK) techniques to reveal correlates and patterns of COVID-19 Disease outbreak in sub-Saharan Africa (SSA).MethodsWe analyzed time series aggregate data compiled by Johns Hopkins University on the outbreak of COVID-19 disease across SSA. COVID-19 data was merged with additional data on socio-demographic and health indicator survey data for 39 of SSA’s 48 countries that reported confirmed cases and deaths from coronavirus between February 28, 2020 through March 26, 2020. We used supervised machine learning algorithm, Lasso for variable selection and statistical inference. EBK was used to also create a raster estimating the spatial distribution of COVID-19 disease outbreak.ResultsThe lasso Cross-fit partialing out predictive model ascertained seven variables significantly associated with the risk of coronavirus infection (i.e. new HIV infections among pediatric, adolescent, and middle-aged adult PLHIV, time (days), pneumococcal conjugate-based vaccine, incidence of malaria and diarrhea treatment). Our study indicates, the doubling time in new coronavirus cases was 3 days. The steady three-day decrease in coronavirus outbreak rate of change (ROC) from 37% on March 23, 2020 to 23% on March 26, 2020 indicates the positive impact of countries’ steps to stymie the outbreak. The interpolated maps show that coronavirus is rising every day and appears to be severely confined in South Africa. In the West African region (i.e. Burkina Faso, Ghana, Senegal, Cote d’Iviore, Cameroon, and Nigeria), we predict that new cases and deaths from the virus are most likely to increase.InterpretationIntegrated and efficiently delivered interventions to reduce HIV, pneumonia, malaria and diarrhea, are essential to accelerating global health efforts. Scaling up screening and increasing COVID-19 testing capacity across SSA countries can help provide better understanding on how the pandemic is progressing and possibly ensure a sustained decline in the ROC of coronavirus outbreak.FundingAuthors were wholly responsible for the costs of data collation and analysis.

Analysis of sampling frequency in groundwater quality monitoring systems: a case study

1994 ◽  
Vol 30 (10) ◽  
pp. 73-78 ◽  
Author(s):  
Andrea Szucs ◽  
Gyözö Jordan
Keyword(s):  
Time Series ◽  
Groundwater Quality ◽  
Sampling Frequency ◽  
Quality Monitoring ◽  
Monitoring Systems ◽  
Additional Information ◽  
Alarm Systems ◽  
Cross Correlation Analysis ◽  
Two Samples ◽  
Groundwater Quality Monitoring

Sampling frequency is one of the most crucial factors in the design of groundwater quality monitoring systems. Monitoring systems in general have two major objectives: (1) to describe natural processes and long-term changes and (2) to serve as alarm-systems and detect single pollution events. A comparison between two data sequences of different sampling frequency - weekly and monthly - is made through an example of the groundwater quality monitoring system in the karstic region of the Transdanubian Mountains in Hungary. Hydrogeochemical time series were first decomposed into their components: trend, periodicity, autocorrelation, and rough in succession. In order to identify outliers within the rough, Exploratory Data Analysis (EDA) was applied. Optimal sampling frequency was determined based on the analysis of the above components. Results have shown that: (1) seasons shorter than two months do exist in the studied time series which cannot be captured by monthly sampling; (2) for monitoring seasonal processes samples should be collected at the Nyquist frequency (at least two samples per period); for pollution detection autocorrelation lag-time (or semi-variogram range in time) should determine the sampling distance; in the lack of autocorrelation property the analysis of outliers should guide the sampling design; (3) cross-correlation analysis between precipitation and the observed parameters indicative of pollutant travel time yields valuable additional information on the pollution sensitivity of the hydrogeological system.

Sign in / Sign up

Export Citation Format

Share Document