Artificial Intelligence Techniques in Hydrology and Water Resources Management

doi:10.3390/books978-3-0365-7784-5

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Artificial Intelligence Techniques in Hydrology and Water Resources Management

Edited by

May 2023

302 pages

ISBN978-3-0365-7785-2 (Hardback)
ISBN978-3-0365-7784-5 (PDF)

https://doi.org/10.3390/books978-3-0365-7784-5

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This book is a reprint of the Special Issue Artificial Intelligence Techniques in Hydrology and Water Resources Management that was published in

Biology & Life Sciences

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Public Health & Healthcare

Summary

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 the mitigation of natural disasters. In recent decades, the application of artificial intelligence (AI) techniques in hydrology and water resources management has led to notable advances. In the face of hydro-geo-meteorological uncertainty, AI approaches have proven to be powerful tools for accurately modeling complex, nonlinear hydrological processes and effectively utilizing various digital and imaging data sources, such as ground gauges, remote sensing tools, and in situ Internet of Things (IoT) devices. 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.

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Keywords

ANN; roadside IoT sensors; simulations of the gridded rainstorms; 2D inundation simulation and real-time error correction; weather types and features; meteorological feature extraction; artificial neural network; self-organizing map (SOM); urban agriculture; resource utilization efficiency; urban northern Taiwan; machine learning; random forest; regression analysis; support vector machine; threshold rainfall; threshold runoff; XGBoost; stochastic rainfall generator; Huff rainfall curve; copula; GeoAI; artificial intelligence; machine learning; hydrological; hydraulic; fluvial; water quality; geomorphic; modeling; anomaly detection; deep reinforcement learning; telemetry water level; time series; ensemble; multi-model ensemble; artificial neural network; random forest; precipitation; forecasting; persian gulf; deep learning; dam inflow; RNN; LSTM; GRU; hyperparameter; rainfall time series; artificial neural networks; Multiple Linear Regression; Chania; LSTM; CNN; ELM; temporary rivers; hydrological extremes; multivariate stochastic model; autoregressive model; Markov model; daily temperature; temperature generator; Bayesian neural network; forecasting uncertainty; multi-step ahead forecasting; probabilistic streamflow forecasting; variational inference; smart microclimate-control system (SMCS); machine learning; system dynamics; water–energy–food nexus; agricultural resilience; machine learning; deep learning; hydroinformatics; hydrological modeling; early warning; uncertainty; sustainability