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Prediction of Artesian Source Depollution in Rainy Season Using an Artificial Neural Network

EasyChair Preprint no. 11426

9 pagesDate: December 1, 2023


The management of water suitable for human consumption is a major concern in urban and peri-urban areas. Water reservoirs are regularly polluted as a result of intense and non-localized rainfall events. Predicting these rapid and complex events is very difficult as the drainage of runoff and wastewater is often poorly mastered with massive manpower, time, cost and data.

An artificial neural network (ANN) is an effective method for time series prediction, analysis and forecasting big data in various science and engineering disciplines. It is also beginning to be used in water management.

This study analyzes the ability of multilayer perceptron ANNs to inhibit the pollution of an artesian source in contact with runoff from the city of Bobo-Dioulasso. The results show that ANNs offer a better performance in terms of water quality prediction of an artesian source.

Keyphrases: Artificial Neural Networks, Big Data, Groundwater, water pollutants

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Diarra Mamadou and Tiendrebeogo B. Telesphore and Séré Abdoulaye and Sanogo Sy Rachid},
  title = {Prediction of Artesian Source Depollution in Rainy Season Using an Artificial Neural Network},
  howpublished = {EasyChair Preprint no. 11426},

  year = {EasyChair, 2023}}
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