Artificial Intelligence Techniques in Hydrology and Water Resources Management

Abstract

The sustainable management of water cycles is crucial in the context of climate change and global warming. It involves managing global, regional, and local water cycles—as well as urban, agricultural, and industrial water cycles—to conserve water resources and their relationships with energy, food, microclimates, biodiversity, ecosystem functioning, and anthropogenic activities. Hydrological modeling is indispensable for achieving this goal, as it is essential for water resources management and mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has made notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, non-linear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoTs). The thirteen research papers published in this Special Issue make significant contributions to long- and short-term hydrological modeling and water resources management under changing environments using AI techniques coupled with various analytics tools. These contributions, which cover hydrological forecasting, microclimate control, and climate adaptation, can promote hydrology research and direct policy making toward sustainable and integrated water resources management.

Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes

“…Additionally, the research by [24] supports the integration of deep learning models, such as combining CNN with LSTM and GRU models, to overcome the limitations of singular models in extracting spatiotemporal features. In fact, artificial intelligence has been used to estimate hydrological forecasts for water resource management [25]. Additionally, CNN has been applied together with convolutional-based long short-term memory neural network (ConvLSTM) and backpropagation neural network (BPNN) for a precision agriculture system [26].…”

Editorial: New tools and techniques for advanced water resource management

“…Globally, there is an urgent need to find sustainable solutions to the increasing scarcity of freshwater resources. With more advanced tools and techniques for assessing water resources and water quality, including Artificial Neural Networks (Lohani and Krishan, 2015), Machine Learning (Ghobadi and Kang, 2023), and Artificial Intelligence (Chang et al, 2023), there are possibilities to evaluate water-related issues and find the proper optimal management solutions for their sustainability. This Research Topic contributes to the global challenge of achieving water security by presenting new insights and tools that can strengthen water management and policy.…”

Predicting the Overflowing of Urban Personholes Based on Machine Learning Techniques

“…However, this method requires complicated computations, which take a great deal of time, and might fail to provide an instant warning. Presently, researchers have been exploring the application of artificial intelligence (AI) technologies, particularly machine learning (ML), to address this issue [10,11]. The machine learning technique can be classified as a datadriven approach, since it requires a great deal of training data to compute the statistical relationship between the input and output data.…”