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 which 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.

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 which 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.

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The advancements and new techniques in information technologies are making it possible to acquire large-scale spatial data through satellites, radars and sensor networks. The collection of vast amounts of environmental data increased the demand for applications which can manage and process large-scale and high-resolution data sets in real-time. One of the important tasks for organizing and customizing hydrological data sets is the delineation of watersheds on demand. Watershed delineation is a process for creating a boundary that represents the contributing area for a specific control point or water outlet, with the intent of characterization and analysis of portions of a study area. Although many GIS tools and software are available for watershed analysis on desktop systems, there is a need for optimized libraries for client-side and server-side web applications for creating a dynamic and interactive environment for exploring hydrological data. In this project, we developed and demonstrated several watershed delineation techniques on the web, with seven different use cases implemented on the client-side using JavaScript, WebAssembly, and WebGL and on the server-side using Python, Go, C, and Node.js. We also developed a client-side GPGPU (General Purpose Graphical Processing Unit) algorithm to analyze high-resolution terrain data for watershed delineation by benefiting from the parallelizable nature of GPUs. The web-based real-time analysis of watershed segmentation can be helpful for decision-makers and stakeholders while eliminating the need of installing complex software packages and dealing with large-scale data sets.

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