Natural-society multiple entities interaction modeling and watershed hazard risk estimation

With the urban extension, land development, land use change, and climate change, the increasing natural-society interaction breeds the environment of hazard risk for human society. Watershed hazard risk assessment has been continuously researched with changed natural environment and human activity. This paper has discussed integrated assessment approaches based on watershed natural-society dynamic interaction modeling and estimation methods. Compared with traditional multiple criteria or multiple attribute assessment, the proposed approaches consider the characteristics of natural-society multiple entities interaction and dynamic change environment. A formal representation of natural-society multiple entities and a graph model for multiple entities interaction has been given. Estimation methods based on the multiple entities interaction model have been presented, including single entity state estimation, multiple entities attribute assessment, multiple entities state estimation, and uncertainty multiple entities attribute (or state) Bayesian network estimation. Finally, a case of watershed hydrology-engineering-society interaction and flood hazard risk assessment has been discussed.

[1]  Zoran Kapelan,et al.  Comparative Analysis of System Dynamics and Object-Oriented Bayesian Networks Modelling for Water Systems Management , 2013, Water Resources Management.

[2]  Nir Friedman,et al.  Probabilistic Graphical Models - Principles and Techniques , 2009 .

[3]  Marco Grzegorczyk,et al.  Non-homogeneous dynamic Bayesian networks for continuous data , 2011, Machine Learning.

[4]  M. Ernst,et al.  When correlation implies causation in multisensory integration , 2012 .

[5]  Robert Haining,et al.  Spatial Data Analysis: Theory and Practice , 2003 .

[6]  Gloria Bordogna,et al.  A flexible multi‐source spatial‐data fusion system for environmental status assessment at continental scale , 2008, Int. J. Geogr. Inf. Sci..

[7]  Santiago Ontañón,et al.  A Dynamic-Bayesian Network framework for modeling and evaluating learning from observation , 2014, Expert Syst. Appl..

[8]  Robert G. Raskin,et al.  Knowledge representation in the semantic web for Earth and environmental terminology (SWEET) , 2005, Comput. Geosci..

[9]  Michael F. Goodchild,et al.  Towards a general theory of geographic representation in GIS , 2007, Int. J. Geogr. Inf. Sci..

[10]  J. Pearl Causal inference in statistics: An overview , 2009 .

[11]  J. Junkins,et al.  Optimal Estimation of Dynamic Systems , 2004 .

[12]  Jim W. Hall,et al.  Quantified scenarios analysis of drivers and impacts of changing flood risk in England and Wales: 2030–2100 , 2003 .

[13]  Günter Blöschl,et al.  Socio-hydrology: conceptualising human-flood interactions , 2013 .

[14]  Jim W. Hall,et al.  Fluvial flood risk management in a changing world , 2010 .