1. Αποθετήριο Παραδοτέων Υποτρόφων Δράσεων ΕΣΠΑ Τριτοβάθμιας Εκπαίδευσης 2017-2023
2. Όλο το Αποθετήριο
3. Ερευνητικές ομάδες

Τύπος:	Άρθρο σε επιστημονικό περιοδικό
Τίτλος:	Machine learning for groundwater pollution source identification and monitoring network optimization
Συγγραφέας:	[EL] Κοντός, Γιάννης[EN] Kontos, Yiannis [EL] Κάσσανδρος, Θεοδόσης[EN] Kassandros, Theodosios [EL] Περήφανος, Κωνσταντίνος[EN] Perifanos, Konstantinos [EL] Καραμπάσης, Μάριος[EN] Karampasis, Marios [EL] Κατσιφαράκης, Κωνσταντίνος[EN] Katsifarakis, Konstantinos [EL] Καρατζάς, Κώστας[EN] Karatzas, Kostas
Ημερομηνία:	26/06/2022
Περίληψη:	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 9 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.
Γλώσσα:	Αγγλικά
Σελίδες:	31
DOI:	10.1007/s00521-022-07507-8
EISSN:	1433-3058
Θεματική κατηγορία:	[EL] Περιβαλλοντική μηχανική[EN] Environmental Engineering [EL]  Επιστήμη και Τεχνολογία υδάτων[EN] Water Science and Technology [EL] Ηλεκτρονικοί υπολογιστές στις γεωεπιστήμες[EN] Computers in Earth Sciences [EL] Υδάτινοι πόροι[EN] Water Resources [EL] Επιστήμη των υδάτων[EN] Aquatic Science [EL] Γεωεπιστήμες, διεπιστημονική προσέγγιση[EN] Geosciences, interdisciplinary [EL] Τεχνητή νοημοσύνη[EN] Artificial Intelligence [EL] Έλεγχος και Βελτιστοποίηση[EN] Control and Optimization
Λέξεις-κλειδιά:	Machine learning; Groundwater pollution; Source identification; Monitoring network; Convolutional Neural Networks; Modflow; artificial intelligence; particle tracking; optimal groundwater pollution monitoring network; deep learning
Κάτοχος πνευματικών δικαιωμάτων:	© the Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022
Διατίθεται ανοιχτά στην τοποθεσία:	https://www.researchgate.net/publication/361550232_Machine_Learning_for_Groundwater_Pollution_Source_Identification_and_Monitoring_Network_Optimization
Ηλεκτρονική διεύθυνση του τεκμηρίου στον εκδότη:	https://link.springer.com/article/10.1007/s00521-022-07507-8
Ηλεκτρονική διεύθυνση περιοδικού:	https://www.springer.com/journal/521
Τίτλος πηγής δημοσίευσης:	Neural Computing and Applications
Τεύχος:	22
Τόμος:	34
Σελίδες τεκμηρίου (στην πηγή):	19515–19545
Σημειώσεις:	This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme ‘‘Human Resources Development, Education and Lifelong Learning 2014-20’’ in the context of the project ‘‘Evolution of Computational Intelligence in Environmental Engineering-Generalization, Improvement, Optimal Combination of Methodologies in Air Quality & Water Resources Problems’’ (MIS 5052163).
Εμφανίζεται στις συλλογές:	Ερευνητικές ομάδες