Special Issue "Flood Forecasting Using Machine Learning Methods"
Deadline for manuscript submissions: closed (31 August 2018)
A printed edition of this Special Issue is available here.
Prof. Fi-John Chang
Distinguished Professor, Department of Bioenvironmental Systems Engineering, National Taiwan University, Taiwan
Website | E-Mail
Interests: artificial intelligence; artificial neural network; hydrology; water resources management; ecohydrology; real-time flood forecasting; system analysis; multi-objective reservoir operation; water–food–energy nexus
The degree and scale of flood hazards, nowadays, increases massively with the changing climate, and large-scale floodings jeopardize lives and property, accompanied by great economic losses, in inundation-prone areas of the world. Early flood warning systems with different lead times are promising countermeasures against flood hazards and losses. A collaborative assessment from multiple disciplines, comprising hydrology, remote sensing and meteorology, of the magnitude and impacts of flood hazards on inundation areas beneficially contributes to model integrity and the precision of flood forecasting. Emerging advances in computing technologies, coupled with big-data mining, have boosted data-driven applications, among which Machine Learning (ML) technology bearing flexibility and scalability in pattern extraction has modernized not only scientific thinking but also predictive applications.
In the context of flood hazard mitigation, methodologically-oriented countermeasures may involve forecasting on reservoir inflow, river flow, tropical cyclone track, and flooding at different lead times and/or scales through modern technologies such as, but not limited to, MLs, big-data mining, multiple data aggregation/ensembling, and model ensembling. Analyses of impacts, risks, uncertainty, vulnerability, resilience and scenarios coupled with policy-oriented suggestions will give insight into flood hazard mitigation. A geological information system (GIS) for visual presentation of inundation is also essential and helpful in decision-making.
This Special Issue of Water aims at exploring recent advances on flood management in a timely manner, and interdisciplinary approaches to modelling the complexity of flood hazards-related issues are welcomed. We also encourage contributions in integrative solutions at local, regional or global perspectives.
Prof. Fi-John Chang
Prof. Kuolin Hsu
Prof. Li-Chiu Chang
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Artificial Intelligence (AI)
- Artificial Neural Network(ANN)
- Machine Learning(ML)
- Big Data
- Water Resources Management
- Flood Inundation Forecast
- Flood Early Warning System
- Geological Information System (GIS)
- Remote Sensing