Advances in Hydrologic Forecasts and Water Resources Management

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...

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Bibliographic Details
Other Authors:	Chang, Fi-John (Editor), Guo, Shenglian (Editor)
Format:	Book Chapter
Published:	Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2020
Subjects:	Research & information: general 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 Internet of Things (IoT) regional flood inundation depth recurrent nonlinear autoregressive with exogenous inputs (RNARX) artificial intelligence machine learning multi-objective reservoir operation hydrologic forecasting uncertainty risk
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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.
Physical Description:	1 electronic resource (272 p.)
ISBN:	books978-3-03936-805-1 9783039368044 9783039368051
Access:	Open Access

Advances in Hydrologic Forecasts and Water Resources Management

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