Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh C.; Fijani, Elham; Tziritis, Evangelos

doi:10.1016/j.scitotenv.2017.11.185

Cited by 157 publications

(49 citation statements)

References 62 publications

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“…Sattlecker et al (2014) made use of machine learning method to recognize diagnostic Figure 1. The application fields of machine learning (Azqueta-Gavald on, 2017; Barzegar et al, 2017;Castiglioni et al, 2018;Chalouhi et al, 2017;Goh and Singh, 2015;Karim et al, 2018;Kim et al, 2017;Lázaro et al, 2017;Lee et al, 2017;Pham et al, 2016;Samant and Agarwal, 2018;Sattlecker et al, 2014;Shirzadi et al, 2017;Zeng et al,2018b;Zhang et al,2018a). spectral patterns in clinical practice, and emphasized the importance of the routine spectral data for the reanalysis process.…”

Section: Development Backgroundmentioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

114

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It is of great significance to achieve the prediction of building energy consumption. However, machine learning, as a promising technique for many practical applications, was rarely utilized in this field. The most important reason is that the predictive structure with best performance is difficult to be determined. To fill the gap, this paper offers one in-depth review, which focuses on the accuracy analyses and model comparisons. Specifically, the accuracy analyses were conducted based on different types of buildings (e.g. residential building, commercial building, government building or educational building), different type of temporal granularity (e.g. sub-hourly, hourly, daily or annual), as well as input/output variables and historical data collections. Further, artificial neural network (ANN) and support vector machine (SVM), as the epidemic models, were compared in terms of their complexity of prediction processes, accuracies of results, the amounts of required historical data, the numbers of inputs, etc. Then the hybrid and single machine learning methods were outlined and compared in terms of their strengths and weaknesses. In addition, several vital defects and further research directions are presented from a multivariate perspective. We hope

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Section: Development Backgroundmentioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

114

View full text Add to dashboard Cite

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“…Ensemble modelling is the combination of multiple predictive models that are used conjunctively to provide an evaluation of the same dataset [39]. Ensemble modelling approaches have been widely used in different fields, including groundwater [40][41][42][43][44][45][46][47], flood [40,[48][49][50][51][52][53][54][55][56][57], landslide hazard , land/ground subsidence [88,89], gully erosion [90,91], dust storm [92], wildfire [93], sinkhole [94], droughtiness [95,96], earthquake [97,98] and species distribution [99][100][101]. These studies provide clear evidence that the application of ensemble models can potentially result in improved capability (over individual methods) of GIS-based statistical models [102].…”

Section: Introductionmentioning

confidence: 99%

Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models

Nhu

Rahmati

Falah

et al. 2020

Water

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Groundwater is an important natural resource in arid and semi-arid environments, where discharge from karst springs is utilized as the principal water supply for human use. The occurrence of karst springs over large areas is often poorly documented, and interpolation strategies are often utilized to map the distribution and discharge potential of springs. This study develops a novel method to delineate karst spring zones on the basis of various hydrogeological factors. A case study of the Bojnourd Region, Iran, where spring discharge measurements are available for 359 sites, is used to demonstrate application of the new approach. Spatial mapping is achieved using ensemble modelling, which is based on certainty factors (CF) and logistic regression (LR). Maps of the CF and LR components of groundwater potential were generated individually, and then, combined to prepare an ensemble map of the study area. The accuracy (A) of the ensemble map was then assessed using area under the receiver operating characteristic curve. Results of this analysis show that LR (A = 78%) outperformed CF (A = 67%) in terms of the comparison between model predictions and known occurrences of karst springs (i.e., calibration data). However, combining the CF and LR results through ensemble modelling produced superior accuracy (A = 85%) in terms of spring potential mapping. By combining CF and LR statistical models through ensemble modelling, weaknesses in CF and LR methods are offset, and therefore, we recommend this ensemble approach for similar karst mapping projects. The methodology developed here offers an efficient method for assessing spring discharge and karst spring potentials over regional scales.

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“…The weights of different factors were measured and assigned based on personal judgment and local information [20]. Other assessments using machine-learning models such as decision trees, fuzzy logic, and numerical modeling which provided more sophisticated results [21][22][23]. To derive additional thematic layers, the integration of remote sensing and geophysical surveys with GIS has been attempted [15,24,25].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea

2019

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Recently, data mining analysis techniques have been developed, as large spatial datasets have accumulated in various fields. Such a data-driven analysis is necessary in areas of high uncertainty and complexity, such as estimating groundwater potential. Therefore, in this study, data mining of various spatial datasets, including those based on remote sensing data, was applied to estimate groundwater potential. For the sustainable development of groundwater resources, a plan for the systematic management of groundwater resources should be established based on a quantitative understanding of the development potential. The purpose of this study was to map and analyze the groundwater potential of Goyang-si in Gyeonggi-do province, South Korea and to evaluate the sensitivity of each factor by applying data mining models for big data analysis. A total of 876 surveyed groundwater pumping capacity data were used, 50% of which were randomly classified into training and test datasets to analyze groundwater potential. A total of 13 factors extracted from satellite-based topographical, land cover, soil, forest, geological, hydrogeological, and survey-based precipitation data were used. The frequency ratio (FR) and boosted classification tree (BCT) models were used to analyze the relationships between the groundwater pumping capacity and related factors. Groundwater potential maps were constructed and validated with the receiver operating characteristic (ROC) curve, with accuracy rates of 68.31% and 69.39% for the FR and BCT models, respectively. A sensitivity analysis for both models was performed to assess the influence of each factor. The results of this study are expected to be useful for establishing an effective groundwater management plan in the future.

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Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms

Cited by 157 publications

References 62 publications

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Mapping of Groundwater Spring Potential in Karst Aquifer System Using Novel Ensemble Bivariate and Multivariate Models

Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea

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