A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources (2007.12269v1)

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.

• The paper systematically reviews 129 publications on deep learning in hydrology and water resources, identifying prevalent architectures like CNN and LSTM and common applications such as flood forecasting and water quality prediction.
• A key finding is the significant challenge posed by the lack of standardized, accessible datasets in hydrology, hindering reproducibility and collaborative progress in applying deep learning models.
• The review highlights the potential for deep learning to enhance water management strategies but stresses the critical need for collaborative dataset development, improved model documentation, and addressing ethical considerations.

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

This systematic review provides an in-depth analysis of the integration of deep learning methodologies within the field of hydrology and water resources, addressing the rapidly increasing data volume and variety in this sector. The paper critically examines how emerging deep learning tools have transformed data into actionable knowledge across various water-related applications, such as flood prediction, water quality monitoring, and resource management.

Overview

The article meticulously surveys literature covering deep learning applications across subdomains in hydrology, identifying architectures, data sources, and methodologies along with their applications in addressing water-related challenges. With comprehensive analyses of 129 publications, it reveals emerging trends and key architectures predominantly utilized, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs). These architectures are extensively employed due to their superiority in managing complex time-series data inherent in hydrological processes.

Key Findings

• Architectures: While a wide range of deep learning architectures are explored, CNN and LSTM models emerge as the most adopted, given their capacity to handle spatiotemporal data efficiently. However, the review notes a relative paucity in utilizing architectures like Generative Adversarial Networks (GANs) and Deep Q Networks (DQNs) which could offer innovative approaches.
• Datasets: A significant discovery is the lack of standardized, published datasets in hydrology that hampers reproducibility and collective progress. Most datasets are sourced from governmental bodies and are not readily available for benchmarking, demonstrating a data accessibility challenge.
• Applications: The review highlights flood forecasting and water quality prediction as the primary focus areas for deep learning applications. This trend is likely due to the availability of continuous real-time data and the critical need for predictive capabilities in these areas to mitigate risks.
• Challenges: Key challenges include limited data and lack of interoperability among datasets, along with the potential misuse of “deep learning” as a buzzword without the rigorous application of its principles. Moreover, detailed descriptions of models and methods are often insufficient, which may impact the reproducibility of results.

Implications and Future Directions

The integration of deep learning within hydrology holds substantial implications for enhanced water management strategies, enabling more accurate forecasting models and efficient data utilization. The review emphasizes the need for collaborative dataset development and standardization to improve the robustness and reliability of deep learning models. Furthermore, addressing ethical considerations in the deployment of these systems is crucial given their potential influence on decision-making in disaster risk management and resource allocation.

Future advancements may focus on automated forecasting systems, AI platforms for hydrology, and edge computing to harness real-time data processing at lower costs. The potential to develop virtual and augmented reality applications also holds promise for training and educational purposes within hydrological contexts.

In conclusion, the paper presents a thorough examination of current practices and challenges in applying deep learning to hydrology. It provides practical recommendations to foster innovation and suggests a trajectory for continuous improvement in this evolving intersection of water science and artificial intelligence.