Designing and validating a groundwater sampling campaign in an unmonitored aquifer: Patiño aquifer case

Báez, Liz; Ávalos, Claudia; Lücken, Christian von; Villalba, Cynthia; Nogues, J. P.

doi:10.1007/s12665-021-09706-3

Cited by 1 publication

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References 30 publications

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“…TN concentrations were obtained from interpolating 72 wells distributed in the urban area. The TN concentrations of these wells were extracted from a campaign carried out in 2018 (Báez et al, 2021).…”

Section: Data Preparationmentioning

confidence: 99%

Estimating Contamination Risk Using Artificial Intelligence Models a Case of the Patiño Aquifer, Paraguay

2021

Proceedings of the International Conferences on WWW/Internet 2021 and Applied Computing 2021

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Studying the risk of contamination is essential for the protection of aquifers. In Paraguay, one of the major drinking water supplies is the Patiño Aquifer. A previous study, using a deterministic model, identified that 42% of the aquifer have a high risk of contamination. This work uses artificial intelligence (AI) models, with regression and classification approaches, to estimate the contamination risk of the urban zone of the Patiño aquifer by Total Nitrogen (TN). The Supervised Committee Machine with Artificial Intelligence (SCMAI) model is applied as a regression model, which combines the Artificial Neural Network (ANN), Mamdani Fuzzy Logic (MFL), Sugeno Fuzzy Logic (SFL) and Neuro Fuzzy (NF) models. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and the correlation of estimated risk with TN concentration are used for the evaluation. The Decision Trees (DT), Bayesian Network (BN) and K-Nearest Neighbor (KNN) models are used for the low and not low risk classification approach. These models were evaluated based on the precision, recall and accuracy indicators. The SCMAI model improved the performance and correlation of the ANN, MFL and SFL models, with RMSE, MAE and correlation values of 2.33, 1.38 and 0.86 respectively. The J48 and PART algorithms, applied to the DT model, and the KNN model obtained an accuracy of 99%, and the BN model obtained an accuracy of 98%. Precision and recall values showed that the DT algorithms failed less, by 10%, and that it is able to identify 81% of the cases of the not low risk levels. It was observed that both approaches give a competent picture of the state of the Patiño aquifer in relation to the risk of contamination.

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Section: Data Preparationmentioning

confidence: 99%

Estimating Contamination Risk Using Artificial Intelligence Models a Case of the Patiño Aquifer, Paraguay

2021

Proceedings of the International Conferences on WWW/Internet 2021 and Applied Computing 2021

View full text Add to dashboard Cite

show abstract

Designing and validating a groundwater sampling campaign in an unmonitored aquifer: Patiño aquifer case

Cited by 1 publication

References 30 publications

Estimating Contamination Risk Using Artificial Intelligence Models a Case of the Patiño Aquifer, Paraguay

Estimating Contamination Risk Using Artificial Intelligence Models a Case of the Patiño Aquifer, Paraguay

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