o:5126 Deep neural network models for dynamic resilience estimation of a complex water system under hazards en Abstract The paper investigates feed-forward deep neural networks (DNNs) for estimating dynamic resilience of water resource system affected by unpredictable and dangerous events. Besides different architecture of DNNs, hyperaffect the performance of DNNs. The aim of this research was to investigate the capabilities of DNNs in domain of water resources resilience estimation to provide significantly better results than currently developed ANN models from literature. The DNN models were trained and tested using large, generated dataset related to the Pirot water system. In order to generate data, an appropriate model of system dynamics was used alongside MonteCarlo simulations. The dataset contained two hazardous events: flood and earthquake defined in wide range of situations (nearby 2,000), from moderate to severe ones. The efficacy of examined DNNs were evaluated using average error metric as well as time required for training and execution. Keywords: deep neural networks, dynamic resilience, water resources 1552100 978-86-81037-79-9 2024-07-31T10:23:09.734Z 44 no 46 Vukašin Ćirović Institut za vodoprivredu "Jaroslav Černi"; Jaroslav Černi Water Institute 0009-0006-1980-5986 Milan Stojković Institut za veštačku inteligenciju, Institute for Artifical Intelligence R&D person 0000-0002-7817-9341 Vladimir Milivojević Institut za vodoprivredu "Jaroslav Černi"; Jaroslav Černi Water Institute person 0009-0005-9113-9896 application/pdf 2596598 https://unilib.phaidrabg.rs/o:5126 no yes 1 70 92000004 71A04 3rd Serbian International Conference on Applied Artificial Intelligence (SICAAI), Book of Abstracts University of Kragujevac, Serbia 2024