| Titre : |
Mapping groundwater vulnerability to nitrate contamination using machine learning techniques |
| Type de document : |
document électronique |
| Auteurs : |
Ouassim Benaroussi, Auteur ; Meroua Djellal, Auteur ; Salah Eddine Tachi, Directeur de thèse ; Meriem Chetibi, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2022 |
| Importance : |
1 fichier PDF (3.66 MO) |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Hydraulique : Alger, École Nationale Polytechnique : 2022.
Bibliogr. f. 76-79
Mémoire confidentiel 1 an jusqu'à 2024 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Groundwater vulnerability
Eastern Mitidja
Nitrate
AdaBoost
Random Forest |
| Index. décimale : |
PH00422 |
| Résumé : |
The evaluation of groundwater vulnerability to contamination in the eastern Mitidja aquifer has become very important for water resources control and preservation. This study aims to model the spatial groundwater vulnerability to nitrate based on the maximum acceptable concentration in drinking water (50 mg/L) by using 10 influencing parameters, which are rainfall, vadose zone, depth to groundwater, slope, permeability, distance to river, drainage density, land use, NDVI and TWI. The dataset was randomly divided between training (70%) and validation (30%). We compared between the results of Random Forest and AdaBoost machine learning models, based on the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) equals 86% and 94%, respectively.
In addition, both ML models revealed that rainfall, permeability, and depth to groundwater are the key factors determining groundwater vulnerability to nitrate (NO3) in the eastern Mitidja and it also predicted indexes for each parameter based on their importance. As a result, the groundwater vulnerability map was elaborated. |
Mapping groundwater vulnerability to nitrate contamination using machine learning techniques [document électronique] / Ouassim Benaroussi, Auteur ; Meroua Djellal, Auteur ; Salah Eddine Tachi, Directeur de thèse ; Meriem Chetibi, Directeur de thèse . - [S.l.] : [s.n.], 2022 . - 1 fichier PDF (3.66 MO). Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Hydraulique : Alger, École Nationale Polytechnique : 2022.
Bibliogr. f. 76-79
Mémoire confidentiel 1 an jusqu'à 2024 Langues : Anglais ( eng)
| Mots-clés : |
Groundwater vulnerability
Eastern Mitidja
Nitrate
AdaBoost
Random Forest |
| Index. décimale : |
PH00422 |
| Résumé : |
The evaluation of groundwater vulnerability to contamination in the eastern Mitidja aquifer has become very important for water resources control and preservation. This study aims to model the spatial groundwater vulnerability to nitrate based on the maximum acceptable concentration in drinking water (50 mg/L) by using 10 influencing parameters, which are rainfall, vadose zone, depth to groundwater, slope, permeability, distance to river, drainage density, land use, NDVI and TWI. The dataset was randomly divided between training (70%) and validation (30%). We compared between the results of Random Forest and AdaBoost machine learning models, based on the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) equals 86% and 94%, respectively.
In addition, both ML models revealed that rainfall, permeability, and depth to groundwater are the key factors determining groundwater vulnerability to nitrate (NO3) in the eastern Mitidja and it also predicted indexes for each parameter based on their importance. As a result, the groundwater vulnerability map was elaborated. |
|
PH00100 
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