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Water Sci Technol ; 82(12): 2635-2670, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-33341760


The global volume of digital data is expected to reach 175 zettabytes by 2025. The volume, variety and velocity of water-related data are increasing due to large-scale sensor networks and increased attention to topics such as disaster response, water resources management, and climate change. Combined with the growing availability of computational resources and popularity of deep learning, these data are transformed into actionable and practical knowledge, revolutionizing the water industry. In this article, a systematic review of literature is conducted to identify existing research that incorporates deep learning methods in the water sector, with regard to monitoring, management, governance and communication of water resources. The study provides a comprehensive review of state-of-the-art deep learning approaches used in the water industry for generation, prediction, enhancement, and classification tasks, and serves as a guide for how to utilize available deep learning methods for future water resources challenges. Key issues and challenges in the application of these techniques in the water domain are discussed, including the ethics of these technologies for decision-making in water resources management and governance. Finally, we provide recommendations and future directions for the application of deep learning models in hydrology and water resources.

Aprendizado Profundo , Recursos Hídricos , Mudança Climática , Hidrologia
Sci Total Environ ; 728: 138895, 2020 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-32361365


With increasing population and human intervention on the natural environment, hazards are a growing threat, coming in many forms, including floods, droughts, soil erosion, and water pollution. A key approach to mitigate hydrological disaster risk at the community level is informed planning with decision support systems. The literature shows emerging efforts on multi-hazard decision support systems for hydrological disasters and demonstrates the need for an engaging, accessible, and collaborative serious game environment facilitating the relationship between the environment and communities. In this study, a web-based decision support tool (DST) was developed for hydrological multi-hazard analysis while employing gamification techniques to introduce a competitive element. The serious gaming environment provides functionalities for intuitive management, visualization, and analysis of geospatial, hydrological, and economic data to help stakeholders in the decision-making process regarding hydrological hazard preparedness and response. Major contributions of the presented DST include involving the community in environmental decision making by reducing the technical complexity required for analysis, increasing community awareness for the environmental and socio-economic consequences of hydrological hazards, and allowing stakeholders to discover and discuss potential trade-offs to hazardous scenarios considering the limitations in budget, regulations, and technicality. The paper describes the software design approaches and system architecture applied for a modular, secure, and scalable software as well as the framework's intuitive web-based user interfaces for real-time and collaborative data analysis and damage assessment. Finally, a case study was conducted to demonstrate the usability of DST in a formal setting and to measure user satisfaction with surveys.