URI | http://purl.tuc.gr/dl/dias/29A16456-229D-4D5C-871E-2D325B253D5E | - |
Αναγνωριστικό | https://doi.org/10.3390/w15152829 | - |
Αναγνωριστικό | https://www.mdpi.com/2073-4441/15/15/2829 | - |
Γλώσσα | en | - |
Μέγεθος | 18 pages | en |
Τίτλος | Classification of WatSan technologies using machine learning techniques | en |
Δημιουργός | Al Nuaimi Hala | en |
Δημιουργός | Abdelmagid Mohamed | en |
Δημιουργός | Bouabid Ali | en |
Δημιουργός | Chrysikopoulos Constantinos | en |
Δημιουργός | Χρυσικοπουλος Κωνσταντινος | el |
Δημιουργός | Maalouf Maher | en |
Εκδότης | MDPI | en |
Περίληψη | A substantial portion of the water supply and sanitation (WatSan) infrastructure in the rural areas of developing countries is currently not operating. This failure is due to the inappropriate implementation of WatSan technologies and the lack of decision-making resources. This study explores the application of several machine learning classification algorithms to predict the optimal WatSan system effectively. The proposed classification methods are Logistic Regression, Random Forest, Support Vector Machine, CatBoost, and Neural Network. The practicality of these classification methods was tested using a dataset comprising 774 water technology options. Several experiments were conducted to obtain the highest possible classification accuracy of the capacity requirement level (CRL) in terms of accuracy and F1 score classification metrics. Our findings suggest that CatBoost, with the addition of the synthetic minority oversampling technique (SMOTE), outperforms the other algorithms in classifying WatSan technology options. | en |
Τύπος | Peer-Reviewed Journal Publication | en |
Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
Ημερομηνία | 2025-07-18 | - |
Ημερομηνία Δημοσίευσης | 2023 | - |
Θεματική Κατηγορία | Classification | en |
Θεματική Κατηγορία | Decision support system | en |
Θεματική Κατηγορία | Logistic Regression | en |
Θεματική Κατηγορία | Machine learning | en |
Θεματική Κατηγορία | Random Forest | en |
Θεματική Κατηγορία | Support Vector Machine | en |
Βιβλιογραφική Αναφορά | H. Al Nuaimi, M. Abdelmagid, A. Bouabid, C. V. Chrysikopoulos and M. Maalouf, “Classification of WatSan technologies using machine learning techniques,” Water, vol. 15, no. 15, Aug. 2023, doi: 10.3390/w15152829. | en |