Dataset Information

Machine learning for groundwater pollution source identification and monitoring network optimization.

ABSTRACT: The identification of the source in groundwater pollution is the only way to drastically deal with resulting environmental problems. This can only be achieved by an appropriate monitoring network, the optimization of which is prerequisite for the solution of the inverse modeling problem, i.e., identifying the source of the pollutant on the basis of measurements taken within the pollution field. For this reason, a theoretical confined aquifer with two pumping wells and six suspected sources is studied. Simulations of combinations of possible source locations, and hydraulic parameters, produce sets of measurement features for a 29 × 29 grid representing potential monitoring wells. Three sets of simulations are conducted to produce synthetic datasets, representing different groundwater pollution modeling methods. Features (input-X variables) coupled with respective sources (output-Y variables) are formulated in two different dataset formats (Types A, B) in order to train classification (random forests, multilayer perceptron) and computer vision (convolutional neural networks) algorithms, respectively, to solve the inverse modeling problem. In addition, appropriate feature selection and trial-and-error tests are employed for supporting the optimization of monitoring wells' number, locations and sampling frequency. The methodology can successfully produce various sub-optimal monitoring strategies for various budgets.

Supplementary information

The online version contains supplementary material available at 10.1007/s00521-022-07507-8.

SUBMITTER: Kontos YN

PROVIDER: S-EPMC9243871 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Monitoring safe water access in developing countries relies primarily on household health survey and census data. These surveys are often incomplete: they tend to focus on the primary water source only, are spatially coarse, and usually happen every 5-10 years, during which significant changes can happen in urbanisation and infrastructure provision, especially in sub Saharan Africa. In this work, we present a data-driven approach that utilises and compliments survey based data of water access, to provide context-specific and disaggregated monitoring. The level of access to improved water and sanitation has been shown to vary with geographical inequalities related to the availability of water resources and terrain, population density and socio-economic determinants such as income and education. We use such data and successfully predict the level of water access in areas for which data is lacking, providing spatially explicit and community level monitoring possibilities for mapping geographical inequalities in access. This is showcased by applying three machine learning models that use such geographical data to predict the number of presences of water access points of eight different access types across Uganda, with a 1km by 1km grid resolution. Two Multi-Layer-Perceptron (MLP) models and a Maximum Entropy (MaxEnt) model are developed and compared, where the former are shown to consistently outperform the latter. The best performing Neural Network model achieved a True Positive Rate of 0.89 and a False Positive Rate of 0.24, compared to 0.85 and 0.46 respectively for the MaxEnt model. The models improve on previous work on water point modeling through the use of neural networks, in addition to introducing the True Positive - and False Positive Rate as better evaluation metrics to also assess the MaxEnt model. We also present a scaling method to move from predicting only the relative probability of water point presences, to predicting the absolute number of presences. To challenge both the model results and the more standard health surveys, a new household level survey is carried out in Bushenyi, a mid-sized town in the South-West of Uganda, asking specifically about the multitude of water sources. On average Bushenyi households reported to use 1.9 water sources. The survey further showed that the actual presence of a source, does not always imply that it is used. Therefore it is no option to rely solely on models for water access monitoring. For this, household surveys remain necessary but should be extended with questions on the multiple sources that are used by households.

Dataset Information

Machine learning for groundwater pollution source identification and monitoring network optimization.

Supplementary information

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