Reprint
Advances in Hydrologic Forecasts and Water Resources Management
Edited by
August 2020
274 pages
- ISBN978-3-03936-804-4 (Hardback)
- ISBN978-3-03936-805-1 (PDF)
This book is a reprint of the Special Issue Advances in Hydrologic Forecasts and Water Resources Management that was published in
Biology & Life Sciences
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Public Health & Healthcare
Summary
The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.
Format
- Hardback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
water resources management; landslide; dammed lake; flood risk; time-varying parameter; GR4J model; changing environments; temporal transferability; western China; cascade hydropower reservoirs; multi-objective optimization; TOPSIS; gravitational search algorithm; opposition learning; partial mutation; elastic-ball modification; Snowmelt Runoff Model; parameter uncertainty; data-scarce deglaciating river basin; climate change impacts; generalized likelihood uncertainty estimation; Yangtze River; cascade reservoirs; impoundment operation; GloFAS-Seasonal; forecast evaluation; small and medium-scale rivers; highly urbanized area; flood control; whole region perspective; coupled models; flood-risk map; hydrodynamic modelling; Sequential Gaussian Simulation; urban stormwater; probabilistic forecast; Unscented Kalman Filter; artificial neural networks; Three Gorges Reservoir; Mahalanobis-Taguchi System; grey entropy method; signal-to-noise ratio; degree of balance and approach; interval number; multi-objective optimal operation model; feasible search space; Pareto-front optimal solution set; loss–benefit ratio of ecology and power generation; elasticity coefficient; empirical mode decomposition; Hushan reservoir; data synthesis; urban hydrological model; Generalized Likelihood Uncertainty Estimation (GLUE); Technique for Order Preference by Similarity to Ideal Solution (TOPSIS); uncertainty analysis; NDVI; Yarlung Zangbo River; machine learning model; random forest; machine learning model; Internet of Things (IoT); regional flood inundation depth; recurrent nonlinear autoregressive with exogenous inputs (RNARX); artificial intelligence; machine learning; water resources management; multi-objective reservoir operation; hydrologic forecasting; uncertainty; risk