An Automated Approach to Groundwater Quality Monitoring—Geospatial Mapping Based on Combined Application of Gaussian Process Regression and Bayesian Information Criterion

Abstract

Sustainable management of the environment is based on the preservation of natural resources, first of all, freshwater—both surface and groundwater—from exhaustion and contamination. Thus, development of adequate monitoring solutions, including fast and adaptive modelling approaches, are of high importance. Recent progress in machine learning techniques provide an opportunity to improve the prediction accuracy of the spatial distribution of properties of natural objects and to automate all stages of this process to exclude uncertainties caused by handcrafting. We propose a technique to construct the weighted Water Quality Index (WQI) and the spatial prediction map of the WQI in tested area. In particular, WQI is calculated using dimensionality reduction technique (Principal Component Analysis), and spatial map of WQI is constructed using Gaussian Process Regression with automatic kernel structure selection using Bayesian Information Criterion (BIC). We validate our approach on a new dataset for groundwater quality in the New Moscow region, where groundwater is mostly used for drinking purposes. According to estimated WQI values, groundwater quality across the study region is relatively high, with few points, less than 0.5% of all observations, severely contaminated. Estimated WQIs then were used to construct spatial distribution models, GPR-BIC approach was compared with ordinary Kriging (OK), Universal Kriging (UK) with exponential, Gaussian, polynomial and periodic kernels. Quality of models was assessed using cross-validation scheme, according to which BIC-GPR approach showed better performance on average with 15% higher R2 score comparing to other Kriging models. We show that the proposed geospatial interpolation is a potentially powerful and adaptable tool for predicting the spatial distribution of properties of natural resources.

Photocatalytic Activity of S-Scheme Heterostructure for Hydrogen Production and Organic Pollutant Removal: A Mini-Review

“…Pharmaceutical active compounds are helpful tools to eradicate human diseases but the pollution induced by the metabolized or partially metabolized pharmaceutical wastes represents an important bio-hazard [60,61]. The solvothermal technique (Figure 4) was used to develop three S-scheme heterostructures for tetracycline (TC) removal: black phosphorus (BP)/BiOBr [62] 4 shows enhanced Vis light absorbance and a direct S-scheme path of charge separation and transfer. However, the TC concentration was 5× lower (10 mg/L) compared with BP nanosheets (E G = 1.68 eV)/BiOBr nanosheets (E G = 2.73 eV) photocatalytic activity, where the efficiency was 85%.…”

“…The water contamination represents an important problem considering that the quantity and complexity of organic pollutant (such as dyes, pesticides, pharmaceutical active compounds, etc.) molecules can not be efficiently addressed by the traditional wastewater treatments [3,4]. The use of fossil fuels has disadvantages such as environmental pollution and limited stock, which inevitably causes energy shortages in various countries [5,6].…”

Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case

“…Wells were selected to be an object for the research since they comprise the largest part of the sample set, 1182 samples remained on the list after outliers have been excluded. The outliers were discarded based on DBSCAN clustering algorithm (this methodology was described in detail in [18]). Each sample was evaluated according to 25 parameters of chemical properties.…”

“…Principal component analysis (PCA) has already been proposed as a means to aggregate all measurements into the final WQI without overestimating parameters [21,22]. In this study, we used the WQI geospatial modeling approach proposed by Shadrin et al [18]. This approach includes two steps: (i) calculating PCA-weighted WQI for each tested sample, (ii) applying Gaussian Process Regression (GPR) with optimal kernel structure search using Bayesian informational criteria in order to build a precise map of water quality distribution.…”

“…To solve the problem of reconstruction, the combination of the basic kernels was used [29,30]. Kernel hyper-parameter was obtained by using Bayesian Information Criteria (BIC) [31] according to [18], the selected kernels are represented by Equations ( 3) and (4) (see Table 1 for the obtained coefficients). The regression model for predicting the water quality index was implemented by using the GPy library for python [32].…”