Advances in Hydrologic Forecasts and Water Resources Management
The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources management and the mitigation of natural disaste...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
|
| Series: | Water |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4441/12/6/1819 |
| _version_ | 1797564240779804672 |
|---|---|
| author | Fi-John Chang Shenglian Guo |
| author_facet | Fi-John Chang Shenglian Guo |
| author_sort | Fi-John Chang |
| collection | DOAJ |
| description | The impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources 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 modelling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has the 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 modelling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; and (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue can not only advance water sciences but can also support policy makers toward more sustainable and effective water resources management. |
| first_indexed | 2024-03-10T18:54:35Z |
| format | Article |
| id | doaj.art-b687d53c82204924bf65b52a958ec338 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T18:54:35Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-b687d53c82204924bf65b52a958ec3382023-11-20T04:53:45ZengMDPI AGWater2073-44412020-06-01126181910.3390/w12061819Advances in Hydrologic Forecasts and Water Resources ManagementFi-John Chang0Shenglian Guo1Department of Bioenvironmental Systems Engineering, National Taiwan University, Taipei 10617, TaiwanState Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, ChinaThe impacts of climate change on water resources management as well as the increasing severe natural disasters over the last decades have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resources 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 modelling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has the 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 modelling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; and (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue can not only advance water sciences but can also support policy makers toward more sustainable and effective water resources management.https://www.mdpi.com/2073-4441/12/6/1819artificial intelligencemachine learningwater resources managementmulti-objective reservoir operationhydrologic forecastinguncertainty |
| spellingShingle | Fi-John Chang Shenglian Guo Advances in Hydrologic Forecasts and Water Resources Management Water artificial intelligence machine learning water resources management multi-objective reservoir operation hydrologic forecasting uncertainty |
| title | Advances in Hydrologic Forecasts and Water Resources Management |
| title_full | Advances in Hydrologic Forecasts and Water Resources Management |
| title_fullStr | Advances in Hydrologic Forecasts and Water Resources Management |
| title_full_unstemmed | Advances in Hydrologic Forecasts and Water Resources Management |
| title_short | Advances in Hydrologic Forecasts and Water Resources Management |
| title_sort | advances in hydrologic forecasts and water resources management |
| topic | artificial intelligence machine learning water resources management multi-objective reservoir operation hydrologic forecasting uncertainty |
| url | https://www.mdpi.com/2073-4441/12/6/1819 |
| work_keys_str_mv | AT fijohnchang advancesinhydrologicforecastsandwaterresourcesmanagement AT shenglianguo advancesinhydrologicforecastsandwaterresourcesmanagement |