Deep learning in water resources management: The case study of Kastoria lake in Greece
dc.creator | Karamoutsou L., Psilovikos A. | en |
dc.date.accessioned | 2023-01-31T08:31:16Z | |
dc.date.available | 2023-01-31T08:31:16Z | |
dc.date.issued | 2021 | |
dc.identifier | 10.3390/w13233364 | |
dc.identifier.issn | 20734441 | |
dc.identifier.uri | http://hdl.handle.net/11615/74407 | |
dc.description.abstract | The effects of climate change on water resources management have drawn worldwide attention. Water quality predictions that are both reliable and precise are critical for an effective water resources management. Although nonlinear biological and chemical processes occurring in a lake make prediction complex, advanced techniques are needed to develop reliable models and effective management systems. Artificial intelligence (AI) is one of the most recent methods for modeling complex structures. The applications of machine learning (ML), as a part of AI, in hydrology and water resources management have been increasing in recent years. In this paper, the ability of deep neural networks (DNNs) to predict the quality parameter of dissolved oxygen (DO), in Lake Kastoria, Greece, is tested. The available dataset from 11 November 2015, to 15 March 2018, on an hourly basis, from four telemetric stations located in the study area consists of (1) Chl-a (µg/L), (2) pH, (3) temperature—Tw (◦C), (4) conductivity (µS/cm), (5) turbidity (NTU), (6) ammonia (NH4, mg/L), (7) nitrate nitrogen (N–NO3, mg/L), and (8) dissolved oxygen (DO) (mg/L). Feed-forward deep neural networks (FF-DNNs) of DO, with different structures, are tested for all stations. All the well-trained DNNs give satisfactory results. The optimal selected FF-DNNs of DO for each station with a high efficiency (NSE > 0.89 for optimal selected structures/station) constitute a good choice for modeling dissolved oxygen. Moreover, they provide information in real time and comprise a powerful decision support system (DSS) for preventing accidental and emergency conditions that may arise from both natural and anthropogenic hazards. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | en |
dc.language.iso | en | en |
dc.source | Water (Switzerland) | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120157342&doi=10.3390%2fw13233364&partnerID=40&md5=1b3700c16b52513f272eba59e9018ac1 | |
dc.subject | Ammonia | en |
dc.subject | Biochemical oxygen demand | en |
dc.subject | Climate change | en |
dc.subject | Complex networks | en |
dc.subject | Decision support systems | en |
dc.subject | Deep neural networks | en |
dc.subject | Dissolution | en |
dc.subject | Forecasting | en |
dc.subject | Lakes | en |
dc.subject | Structural optimization | en |
dc.subject | Water quality | en |
dc.subject | Biological process | en |
dc.subject | Case-studies | en |
dc.subject | Chemical process | en |
dc.subject | Deep learning | en |
dc.subject | Feed forward | en |
dc.subject | Feed-forward network | en |
dc.subject | Lake kastoria | en |
dc.subject | Reliable models | en |
dc.subject | Water quality predictions | en |
dc.subject | Water resources management | en |
dc.subject | Dissolved oxygen | en |
dc.subject | climate change | en |
dc.subject | decision support system | en |
dc.subject | resource management | en |
dc.subject | spatiotemporal analysis | en |
dc.subject | water management | en |
dc.subject | water resource | en |
dc.subject | Greece | en |
dc.subject | Kastoria | en |
dc.subject | Lake Kastoria | en |
dc.subject | Western Macedonia | en |
dc.subject | MDPI | en |
dc.title | Deep learning in water resources management: The case study of Kastoria lake in Greece | en |
dc.type | journalArticle | en |
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