A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources

Abstract

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.

Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea

“…In fact, these problems have already been pointed out in previous studies. For instance, Sit et al (2020) commented that "ethics in disaster management and public planning may arise due to the automation of hydrologic modeling with deep learning" through the title "Ethics in deep learning applications" in the research.…”

“…In the past, the use of water-related information is the key to securing the sustainability and resilience of water resources and has been recognized as an opportunity to innovate water governance in the future (Grossman et al, 2015). In addition, recently, numerous studies have attempted to apply data-driven models such as machine leaning or deep learning techniques for various purposes from water information analysis to hydrological, hydraulic analysis based on vast amount of water-related data (Sit et al, 2020). General hydrologic models conceptualize the physical characteristics of rainfall-runoff relationship by using mathematical equations (Singh, 1998).…”

“…The types of applied techniques are gradually diversifying. For instance, Sit et al (2020) introduced numerous deep learning technique applied to the hydrological studies such as Convolutional neural networks (CNN; LeCun, 1989), Generative adversarial networks (GAN; Goodfellow et al, 2014), Recurrent neural networks (RNN; Pollack, 1990), Long short-term memory (LSTM; Hochreiter and Schmidhuber, 1997), Gated recurrent unit networks (GRU; Cho et al, 2014), Nonlinear autoregressive models (NAR; Lin et al, 1996), Elman Network (Elman, 1990), Autoencoders (Rumelhart et al, 1985), Restricted boltzmann machines (RBM; Hinton, 2002) and deep belief networks (DBN; Hinton, 2009), Extreme learning machines (ELM; Huang et al, 2006), Deep Q networks (DQN; Mnih et al, 2013). Here, noteworthy is that there are various research results showing that the deep learning technique applied to the rainfall-runoff simulation shows better results than the traditional approach.…”

Flood Markup Language – A Standards-based Exchange Language for Flood Risk Communication

“…Traditional flood risk communication and management consist of three action levels; the operating level, planning level (Xu et al, 2020), and design level (Plate, 2002). Flood prediction can be improved by technologies like distributed computing (Agliamzanov et al, 2020), remote sensing, sensor networks, data-driven (Sit et al, 2020, Xiang and, meteorological and hydrodynamic models. By simulating runoff generation, the flood forecast systems can predict the timing, discharge, and height of flood peaks in a river basin, which provide information on the flood levels for delivery and emergency response system (Li et al, 2016;Yildirim et al, 2021).…”

Opportunistic emulation of computationally expensive simulations via Deep Learning

“…To overcome the abovementioned problems, practitioners in recent years have advocated the use of other machine learning approaches such as Random Forests (Hooten et al 2011, Leeds et al 2013, Gladish et al 2018 and Deep Neural Networks (DNNs) (Puscasu 2014, Pal et al 2019, Kasim et al 2020, Sit et al 2020. Standard implementations of Random Forests suffer from the problem that they only predict univariate outputs.…”