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Sci Total Environ ; 761: 144121, 2021 Mar 20.
Artigo em Inglês | MEDLINE | ID: mdl-33360127


Groundwater supplies drinking water for over one-third of all Americans. However, with aquifers stressed by overdraft, contamination from land use, and the hydrologic impacts of climate change, identifying reliable sources for new wells is increasingly challenging. Well forecasting is a process in which potential groundwater resources are evaluated for a location of interest. While this process forecasts the depth of each aquifer for a given location, it takes historical groundwater well data from nearby locations into account. Conventionally, well forecasting is done by geological survey professionals by manually analyzing the well data and, that makes the process both time and resource-intensive. This study presents a novel web application that performs well forecasting for any location within the state of Iowa in a matter of seconds utilizing client-side computing instead of expensive professional labor. The web application generates well forecasts by triangulating millions of combinations of historical aquifer depth data of nearby wells stored in a state-level database. The proposed web system also provides water quality information for arsenic, nitrate, and bacteria (total c and fecal coliform) on the same interface with forecasts. The system is open to the public and is aimed to provide a go-to tool for homeowners, well drillers and, authorities to help inform decision-making regarding groundwater well development and water quality monitoring efforts.

Ciência de Dados , Água Subterrânea , Poluentes Químicos da Água , Monitoramento Ambiental , Humanos , Internet , Iowa , Poluentes Químicos da Água/análise , Poços de Água
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