Journal ArticleDOI
Multi-Criteria decision support systems for flood hazard mitigation and emergency response in urban watersheds
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TLDR
Multi-Criteria Decision Support Systems (MCDSS) as mentioned in this paper can help to manage this complexity and decision load by combining value judgments and technical information in a structured decision framework.Abstract:
Flood management problems are inherently complex, time-bound and multi-faceted, involving many decision makers (with conflicting priorities and dynamic preferences), high decision stakes, limited technical information (both in terms of quality and quantity), and difficult tradeoffs. Multi-Criteria Decision Support Systems (MCDSS) can help to manage this complexity and decision load by combining value judgments and technical information in a structured decision framework. A brief overview of MCDSS is presented, an original MCDSS architecture is put forth, and future research directions are discussed, including extensions to Multi-Criteria Spatial Decision Support Systems and group MCDSS (as flood management involves shared resources and broad constituencies). With application to the September 11-12, 2000 Tokai floods in Japan, the proposed multi-criteria decision support instruments enhance communication among stakeholders and improve emergency management resource allocation. In summary, by making the links among flood knowledge, assumptions and choices more explicit, MCDSS increases stakeholder satisfaction, saves lives, and reduces flood management costs, thereby increasing decision-making effectiveness, efficiency and transparency.read more
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Journal ArticleDOI
Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
TL;DR: In this paper, support vector machine (SVM) is used to predict flood susceptibility in the Kuala Terengganu basin, Malaysia, and four SVM kernel types such as linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG) were used to check the robustness of the SVM model.
Journal ArticleDOI
Enhanced chemical weathering as a geoengineering strategy to reduce atmospheric carbon dioxide, supply nutrients, and mitigate ocean acidification
Jens Hartmann,A. Joshua West,Philip Renforth,Peter Köhler,Christina L. De La Rocha,Dieter Wolf-Gladrow,Hans H. Dürr,Jürgen Scheffran +7 more
TL;DR: Enhanced weathering is an integral part of both the rock and carbon cycles and is being affected by changes in land use, particularly as a result of agricultural practices such as tilling, mineral fertilization, or liming to adjust soil pH as mentioned in this paper.
Journal ArticleDOI
Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
Wei Chen,Wei Chen,Haoyuan Hong,Haoyuan Hong,Shaojun Li,Himan Shahabi,Yi Wang,Xiaojing Wang,Baharin Bin Ahmad +8 more
TL;DR: In this article, the authors employ machine learning-based Reduced Error Pruning Trees (REPTree) with Bagging (Bag-REPTrees) and Random Subspace (RS-REptree) ensemble frameworks for spatial prediction of flood susceptibility using a geographic information system (GIS).
Journal ArticleDOI
Three Points Approach (3PA) for urban flood risk management: a tool to support climate change adaptation through transdisciplinarity and multifunctionality.
TL;DR: The Three Points Approach (3PA) as discussed by the authors provides a structure facilitating the decision making processes dealing with UFRM, which helps to accept the complexity of the urban context and promotes transdisciplinarity and multifunctionality.
Journal ArticleDOI
Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory
Thimmaiah Gudiyangada Nachappa,Sepideh Tavakkoli Piralilou,Khalil Gholamnia,Omid Ghorbanzadeh,Omid Rahmati,Thomas Blaschke +5 more
TL;DR: In this paper, the authors compared the performance of two multi-criteria decision analysis (MCDA) models including analytical hierarchical process (AHP) and analytical network process (ANP) and two machine learning models including random forest (RF) and support vector machine (SVM).
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